Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast
Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variatio...
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| Veröffentlicht in: | PLoS genetics 2015-05, Vol.11 (5), p.e1005206-e1005206 |
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| creator | Csárdi, Gábor Franks, Alexander Choi, David S Airoldi, Edoardo M Drummond, D Allan |
| description | Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena. |
| doi_str_mv | 10.1371/journal.pgen.1005206 |
| format | Article |
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Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.</description><identifier>ISSN: 1553-7404</identifier><identifier>ISSN: 1553-7390</identifier><identifier>EISSN: 1553-7404</identifier><identifier>DOI: 10.1371/journal.pgen.1005206</identifier><identifier>PMID: 25950722</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Competition ; Datasets ; Gene Expression Regulation, Fungal ; Genes ; Genetic aspects ; Identification and classification ; Messenger RNA ; Methods ; Models, Genetic ; Noise ; Physiological aspects ; Proteins ; Reproducibility of Results ; RNA Processing, Post-Transcriptional ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Rodents ; Saccharomyces cerevisiae - genetics ; Saccharomyces cerevisiae - metabolism ; Saccharomyces cerevisiae Proteins - genetics ; Saccharomyces cerevisiae Proteins - metabolism ; Studies ; Transcription (Genetics) ; Transcription, Genetic ; Yeast ; Yeasts (Fungi)</subject><ispartof>PLoS genetics, 2015-05, Vol.11 (5), p.e1005206-e1005206</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Csárdi et al 2015 Csárdi et al</rights><rights>2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Csárdi G, Franks A, Choi DS, Airoldi EM, Drummond DA (2015) Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast. 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Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.</description><subject>Competition</subject><subject>Datasets</subject><subject>Gene Expression Regulation, Fungal</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Identification and classification</subject><subject>Messenger RNA</subject><subject>Methods</subject><subject>Models, Genetic</subject><subject>Noise</subject><subject>Physiological aspects</subject><subject>Proteins</subject><subject>Reproducibility of Results</subject><subject>RNA Processing, Post-Transcriptional</subject><subject>RNA, Messenger - genetics</subject><subject>RNA, Messenger - metabolism</subject><subject>Rodents</subject><subject>Saccharomyces cerevisiae - genetics</subject><subject>Saccharomyces cerevisiae - metabolism</subject><subject>Saccharomyces cerevisiae Proteins - genetics</subject><subject>Saccharomyces cerevisiae Proteins - metabolism</subject><subject>Studies</subject><subject>Transcription (Genetics)</subject><subject>Transcription, Genetic</subject><subject>Yeast</subject><subject>Yeasts (Fungi)</subject><issn>1553-7404</issn><issn>1553-7390</issn><issn>1553-7404</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVk9tq3DAQhk1padK0b1BaQaG00N1KlmTLN4Ul9BAICaSHWyHLY68W2XIkOWSfpq9abXYTdqEXLbqQGH3za_g1k2UvCZ4TWpKPKzf5Qdn52MEwJxjzHBePsmPCOZ2VDLPHe-ej7FkIK4wpF1X5NDvKecVxmefH2e-F1m4aohk61DqP4HYEb3oYorJocCYA8nADygYUlyqi_upigWyK2PABqX60pjXQoHqNRhfiLHo1BO3NGI1LxaHROw0hQIKt8h3YNWoggu_NAChEUM16FqKKsCEjmGGnjdJpDSrE59mTNj0OL3b7Sfbzy-cfp99m55dfz04X5zNdVCLOANMKl6KlRc5rwpM_lLZNjhktKsXKlpRVUVdc0Ea3NQcQjSC6hroRbY2pZvQke73VHa0LcudtkKQQnLCSlzQRZ1uicWolx2SS8mvplJF3Aec7qXw02oJkrMhpmyoCJhgvmFCqxUWdN4wJzOs8aX3avTbVPTQ62e2VPRA9vBnMUnbuJinnVAiSBN7tBLy7niBE2ZugwVo1gJvu6sZECJ4a4CR7s0U7lUozQ-uSot7gcsGIYEVZFlWi5n-h0mqgN9oN0JoUP0h4f5CQmAi3sVNTCPLs-9V_sBf_zl7-OmTf7rHL1KVxGZydNs0XDkG2BbV3IXhoH6wmWG6m6f7H5Waa5G6aUtqr_W96SLofH_oHZPUc-A</recordid><startdate>20150501</startdate><enddate>20150501</enddate><creator>Csárdi, Gábor</creator><creator>Franks, Alexander</creator><creator>Choi, David S</creator><creator>Airoldi, Edoardo M</creator><creator>Drummond, D Allan</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150501</creationdate><title>Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast</title><author>Csárdi, Gábor ; Franks, Alexander ; Choi, David S ; Airoldi, Edoardo M ; Drummond, D Allan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c698t-e039078f3625b1537133fd204369a47f1796b9583dcfb5ee8d81cbebd8fb03c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Competition</topic><topic>Datasets</topic><topic>Gene Expression Regulation, Fungal</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Identification and classification</topic><topic>Messenger RNA</topic><topic>Methods</topic><topic>Models, Genetic</topic><topic>Noise</topic><topic>Physiological aspects</topic><topic>Proteins</topic><topic>Reproducibility of Results</topic><topic>RNA Processing, Post-Transcriptional</topic><topic>RNA, Messenger - genetics</topic><topic>RNA, Messenger - metabolism</topic><topic>Rodents</topic><topic>Saccharomyces cerevisiae - genetics</topic><topic>Saccharomyces cerevisiae - metabolism</topic><topic>Saccharomyces cerevisiae Proteins - genetics</topic><topic>Saccharomyces cerevisiae Proteins - metabolism</topic><topic>Studies</topic><topic>Transcription (Genetics)</topic><topic>Transcription, Genetic</topic><topic>Yeast</topic><topic>Yeasts (Fungi)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Csárdi, Gábor</creatorcontrib><creatorcontrib>Franks, Alexander</creatorcontrib><creatorcontrib>Choi, David S</creatorcontrib><creatorcontrib>Airoldi, Edoardo M</creatorcontrib><creatorcontrib>Drummond, D Allan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Csárdi, Gábor</au><au>Franks, Alexander</au><au>Choi, David S</au><au>Airoldi, Edoardo M</au><au>Drummond, D Allan</au><au>Snyder, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast</atitle><jtitle>PLoS genetics</jtitle><addtitle>PLoS Genet</addtitle><date>2015-05-01</date><risdate>2015</risdate><volume>11</volume><issue>5</issue><spage>e1005206</spage><epage>e1005206</epage><pages>e1005206-e1005206</pages><issn>1553-7404</issn><issn>1553-7390</issn><eissn>1553-7404</eissn><abstract>Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25950722</pmid><doi>10.1371/journal.pgen.1005206</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Competition Datasets Gene Expression Regulation, Fungal Genes Genetic aspects Identification and classification Messenger RNA Methods Models, Genetic Noise Physiological aspects Proteins Reproducibility of Results RNA Processing, Post-Transcriptional RNA, Messenger - genetics RNA, Messenger - metabolism Rodents Saccharomyces cerevisiae - genetics Saccharomyces cerevisiae - metabolism Saccharomyces cerevisiae Proteins - genetics Saccharomyces cerevisiae Proteins - metabolism Studies Transcription (Genetics) Transcription, Genetic Yeast Yeasts (Fungi) |
| title | Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast |
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