Epitranscriptomic rRNA fingerprinting reveals tissue-of-origin and tumor-specific signatures

Mammalian ribosomal RNA (rRNA) molecules are highly abundant RNAs, decorated with over 220 rRNA modifications. Previous works have shown that some rRNA modification types can be dynamically regulated; however, how and when the mammalian rRNA modification landscape is remodeled remains largely unexpl...

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Detalhes bibliográficos
Autores: Milenkovic, Ivan, Cruciani, Sonia, Llovera Nadal, Laia, Lucas, Morghan C., Medina, Rebeca, Pauli, Cornelius, Heid, Daniel, Muley, Thomas, Schneider, Marc A., Klotz, Laura V., Allgäuer, Michael, Lattuca, Ruben, Lafontaine, Denis L. J., Müller-Tidow, Carsten, Novoa, Eva Maria
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2025
País:España
Recursos:Universitat Pompeu Fabra
Repositório:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/69753
Acesso em linha:http://hdl.handle.net/10230/69753
http://dx.doi.org/10.1016/j.molcel.2024.11.014
Access Level:Acceso aberto
Palavra-chave:RNA modifications
Cancer
Classification
Direct RNA sequencing
Epitranscriptome
Fingerprinting
Nanopore
Pseudouridine
rRNA
Descrição
Resumo:Mammalian ribosomal RNA (rRNA) molecules are highly abundant RNAs, decorated with over 220 rRNA modifications. Previous works have shown that some rRNA modification types can be dynamically regulated; however, how and when the mammalian rRNA modification landscape is remodeled remains largely unexplored. Here, we employ direct RNA sequencing to chart the human and mouse rRNA epitranscriptome across tissues, developmental stages, cell types, and disease. Our analyses reveal multiple rRNA sites that are differentially modified in a tissue- and/or developmental stage-specific manner, including previously unannotated modified sites. We demonstrate that rRNA modification patterns can be used for tissue and cell-type identification, which we hereby term "epitranscriptomic fingerprinting." We then explore rRNA modification patterns in normal-tumor matched samples from lung cancer patients, finding that epitranscriptomic fingerprinting accurately classifies clinical samples into normal and tumor groups from only 250 reads per sample, demonstrating the potential of rRNA modifications as diagnostic biomarkers.

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