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Article: mRNA display with library of even-distribution reveals cellular interactors of influenza virus NS1

TitlemRNA display with library of even-distribution reveals cellular interactors of influenza virus NS1
Authors
Issue Date2020
Citation
Nature Communications, 2020, v. 11, n. 1, article no. 2449 How to Cite?
Abstract© 2020, The Author(s). A comprehensive examination of protein-protein interactions (PPIs) is fundamental for the understanding of cellular machineries. However, limitations in current methodologies often prevent the detection of PPIs with low abundance proteins. To overcome this challenge, we develop a mRNA display with library of even-distribution (md-LED) method that facilitates the detection of low abundance binders with high specificity and sensitivity. As a proof-of-principle, we apply md-LED to IAV NS1 protein. Complementary to AP-MS, md-LED enables us to validate previously described PPIs as well as to identify novel NS1 interactors. We show that interacting with FASN allows NS1 to directly regulate the synthesis of cellular fatty acids. We also use md-LED to identify a mutant of NS1, D92Y, results in a loss of interaction with CPSF1. The use of high-throughput sequencing as the readout for md-LED enables sensitive quantification of interactions, ultimately enabling massively parallel experimentation for the investigation of PPIs.
Persistent Identifierhttp://hdl.handle.net/10722/285867
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorDu, Yushen-
dc.contributor.authorHultquist, Judd F.-
dc.contributor.authorZhou, Quan-
dc.contributor.authorOlson, Anders-
dc.contributor.authorTseng, Yenwen-
dc.contributor.authorZhang, Tian hao-
dc.contributor.authorHong, Mengying-
dc.contributor.authorTang, Kejun-
dc.contributor.authorChen, Liubo-
dc.contributor.authorMeng, Xiangzhi-
dc.contributor.authorMcGregor, Michael J.-
dc.contributor.authorDai, Lei-
dc.contributor.authorGong, Danyang-
dc.contributor.authorMartin-Sancho, Laura-
dc.contributor.authorChanda, Sumit-
dc.contributor.authorLi, Xinming-
dc.contributor.authorBensenger, Steve-
dc.contributor.authorKrogan, Nevan J.-
dc.contributor.authorSun, Ren-
dc.date.accessioned2020-08-18T04:56:51Z-
dc.date.available2020-08-18T04:56:51Z-
dc.date.issued2020-
dc.identifier.citationNature Communications, 2020, v. 11, n. 1, article no. 2449-
dc.identifier.urihttp://hdl.handle.net/10722/285867-
dc.description.abstract© 2020, The Author(s). A comprehensive examination of protein-protein interactions (PPIs) is fundamental for the understanding of cellular machineries. However, limitations in current methodologies often prevent the detection of PPIs with low abundance proteins. To overcome this challenge, we develop a mRNA display with library of even-distribution (md-LED) method that facilitates the detection of low abundance binders with high specificity and sensitivity. As a proof-of-principle, we apply md-LED to IAV NS1 protein. Complementary to AP-MS, md-LED enables us to validate previously described PPIs as well as to identify novel NS1 interactors. We show that interacting with FASN allows NS1 to directly regulate the synthesis of cellular fatty acids. We also use md-LED to identify a mutant of NS1, D92Y, results in a loss of interaction with CPSF1. The use of high-throughput sequencing as the readout for md-LED enables sensitive quantification of interactions, ultimately enabling massively parallel experimentation for the investigation of PPIs.-
dc.languageeng-
dc.relation.ispartofNature Communications-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titlemRNA display with library of even-distribution reveals cellular interactors of influenza virus NS1-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41467-020-16140-9-
dc.identifier.pmid32415096-
dc.identifier.pmcidPMC7229031-
dc.identifier.scopuseid_2-s2.0-85084787651-
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.spagearticle no. 2449-
dc.identifier.epagearticle no. 2449-
dc.identifier.eissn2041-1723-

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