Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines

Asier González; Muskan Pandey; Niels Schlusser; Sayanur Rahaman; Meric Ataman; Nitish Mittal; Alexander Schmidt; Attila Becskei; Mihaela Zavolan

doi:10.1038/s41597-025-05861-5

Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines

Asier González^*, Muskan Pandey, Niels Schlusser, Sayanur Rahaman, Meric Ataman, Nitish Mittal, Alexander Schmidt, Attila Becskei, Mihaela Zavolan^*

^*Autor corresponent d’aquest treball

Departament de Bioquímica i de Biologia Molecular

Producció científica: Contribució a revista › Article › Recerca › Avaluat per experts

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La correlació limitada entre els nivells d'ARNm i proteïnes dins de les cèl·lules va destacar la necessitat d'estudiar mecanismes de control translacional. Per desxifrar els factors que determinen les taxes dels passos individuals en la traducció de l'ARNm, actualment s'apliquen enfocaments d'aprenentatge automàtic a grans biblioteques de construccions sintètiques, les propietats són generalment diferents de les dels ARNm endògens. Per omplir aquest buit i així permetre el descobriment d'elements que impulsen la traducció d'ARNm individuals endògens, aquí informem dades multi-òmiques estables i dinàmics de línies cel·lulars de càncer hepàtic humà, específicament (i) dades de perfil ribosòmic de cèl·lules no pertorbades així com després del bloqueig de l'inici de la traducció (run-off ribosomal, per traçar elongació de la traducció), (ii) taxes de síntesi proteica estimades mitjançant aminoàcids marcats amb isòtops estables polsats en cultiu cel·lular (pSILAC), i (iii) càrrega mitjana del ribosoma sobre ARNm individuals determinada pel seqüenciament d'ARNm de fraccions polisòmiques (perfil polisòmic). Aquestes dades permetran prediccions millorades de la producció proteica depenent de la seqüència d'ARNm, que és crucial per a l'enginyeria de l'expressió de proteïnes i el disseny de vacunes basades en ARNm.

Idioma original	Anglès
Número d’article	1520
Pàgines (de-a)	1520
Nombre de pàgines	11
Revista	Scientific Data
Volum	12
Número	1
DOIs	https://doi.org/10.1038/s41597-025-05861-5
Estat de la publicació	Publicada - 30 d’ag. 2025

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title = "Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines",

abstract = "The limited correlation between mRNA and protein levels within cells highlighted the need to study mechanisms of translational control. To decipher the factors that determine the rates of individual steps in mRNA translation, machine learning approaches are currently applied to large libraries of synthetic constructs, whose properties are generally different from those of endogenous mRNAs. To fill this gap and thus enable the discovery of elements driving the translation of individual endogenous mRNAs, we here report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically (i) ribosome profiling data from unperturbed cells as well as following the block of translation initiation (ribosome run-off, to trace translation elongation), (ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and (iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). These data will enable improved predictions of mRNA sequence-dependent protein output, which is crucial for engineering protein expression and for the design of mRNA vaccines.",

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doi = "10.1038/s41597-025-05861-5",

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AU - González, Asier

AU - Pandey, Muskan

AU - Schlusser, Niels

AU - Rahaman, Sayanur

AU - Ataman, Meric

AU - Mittal, Nitish

AU - Schmidt, Alexander

AU - Becskei, Attila

AU - Zavolan, Mihaela

N1 - Publisher Copyright: © The Author(s) 2025.

PY - 2025/8/30

Y1 - 2025/8/30

N2 - The limited correlation between mRNA and protein levels within cells highlighted the need to study mechanisms of translational control. To decipher the factors that determine the rates of individual steps in mRNA translation, machine learning approaches are currently applied to large libraries of synthetic constructs, whose properties are generally different from those of endogenous mRNAs. To fill this gap and thus enable the discovery of elements driving the translation of individual endogenous mRNAs, we here report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically (i) ribosome profiling data from unperturbed cells as well as following the block of translation initiation (ribosome run-off, to trace translation elongation), (ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and (iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). These data will enable improved predictions of mRNA sequence-dependent protein output, which is crucial for engineering protein expression and for the design of mRNA vaccines.

AB - The limited correlation between mRNA and protein levels within cells highlighted the need to study mechanisms of translational control. To decipher the factors that determine the rates of individual steps in mRNA translation, machine learning approaches are currently applied to large libraries of synthetic constructs, whose properties are generally different from those of endogenous mRNAs. To fill this gap and thus enable the discovery of elements driving the translation of individual endogenous mRNAs, we here report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically (i) ribosome profiling data from unperturbed cells as well as following the block of translation initiation (ribosome run-off, to trace translation elongation), (ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and (iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). These data will enable improved predictions of mRNA sequence-dependent protein output, which is crucial for engineering protein expression and for the design of mRNA vaccines.

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KW - Humans

KW - Liver Neoplasms/genetics

KW - Multiomics/methods

KW - Polyribosomes

KW - Protein Biosynthesis

KW - RNA, Messenger/genetics

KW - Ribosomes

KW - Reveals

KW - Quantification

KW - Chain elongation

KW - Gene-expression

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Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines

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