Using protein turnover to expand the applications of transcriptomics

Abstract RNA expression and protein abundance are often at odds when measured in parallel, raising questions about the functional implications of transcriptomics data. Here, we present the concept of persistence, which attempts to address this challenge by combining protein half-life data with RNA e...

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Autores principales:	Marissa A. Smail, James K. Reigle, Robert E. McCullumsmith
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89901697162945a3bec19ce00c8ff520
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spelling	oai:doaj.org-article:89901697162945a3bec19ce00c8ff5202021-12-02T16:23:14ZUsing protein turnover to expand the applications of transcriptomics10.1038/s41598-021-83886-72045-2322https://doaj.org/article/89901697162945a3bec19ce00c8ff5202021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83886-7https://doaj.org/toc/2045-2322Abstract RNA expression and protein abundance are often at odds when measured in parallel, raising questions about the functional implications of transcriptomics data. Here, we present the concept of persistence, which attempts to address this challenge by combining protein half-life data with RNA expression into a single metric that approximates protein abundance. The longer a protein’s half-life, the more influence it can have on its surroundings. This data offers a valuable opportunity to gain deeper insight into the functional meaning of transcriptome changes. We demonstrate the application of persistence using schizophrenia (SCZ) datasets, where it greatly improved our ability to predict protein abundance from RNA expression. Furthermore, this approach successfully identified persistent genes and pathways known to have impactful changes in SCZ. These results suggest that persistence is a valuable metric for improving the functional insight offered by transcriptomics data, and extended application of this concept could advance numerous research fields.Marissa A. SmailJames K. ReigleRobert E. McCullumsmithNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marissa A. Smail James K. Reigle Robert E. McCullumsmith Using protein turnover to expand the applications of transcriptomics
description	Abstract RNA expression and protein abundance are often at odds when measured in parallel, raising questions about the functional implications of transcriptomics data. Here, we present the concept of persistence, which attempts to address this challenge by combining protein half-life data with RNA expression into a single metric that approximates protein abundance. The longer a protein’s half-life, the more influence it can have on its surroundings. This data offers a valuable opportunity to gain deeper insight into the functional meaning of transcriptome changes. We demonstrate the application of persistence using schizophrenia (SCZ) datasets, where it greatly improved our ability to predict protein abundance from RNA expression. Furthermore, this approach successfully identified persistent genes and pathways known to have impactful changes in SCZ. These results suggest that persistence is a valuable metric for improving the functional insight offered by transcriptomics data, and extended application of this concept could advance numerous research fields.
format	article
author	Marissa A. Smail James K. Reigle Robert E. McCullumsmith
author_facet	Marissa A. Smail James K. Reigle Robert E. McCullumsmith
author_sort	Marissa A. Smail
title	Using protein turnover to expand the applications of transcriptomics
title_short	Using protein turnover to expand the applications of transcriptomics
title_full	Using protein turnover to expand the applications of transcriptomics
title_fullStr	Using protein turnover to expand the applications of transcriptomics
title_full_unstemmed	Using protein turnover to expand the applications of transcriptomics
title_sort	using protein turnover to expand the applications of transcriptomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89901697162945a3bec19ce00c8ff520
work_keys_str_mv	AT marissaasmail usingproteinturnovertoexpandtheapplicationsoftranscriptomics AT jameskreigle usingproteinturnovertoexpandtheapplicationsoftranscriptomics AT robertemccullumsmith usingproteinturnovertoexpandtheapplicationsoftranscriptomics
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Using protein turnover to expand the applications of transcriptomics

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