Multi-omics analysis identifies RFX7 targets involved in tumor suppression and neuronal processes.


Journal

Cell death discovery
ISSN: 2058-7716
Titre abrégé: Cell Death Discov
Pays: United States
ID NLM: 101665035

Informations de publication

Date de publication:
03 Mar 2023
Historique:
received: 23 01 2023
accepted: 22 02 2023
revised: 19 02 2023
entrez: 2 3 2023
pubmed: 3 3 2023
medline: 3 3 2023
Statut: epublish

Résumé

Recurrently mutated in lymphoid neoplasms, the transcription factor RFX7 is emerging as a tumor suppressor. Previous reports suggested that RFX7 may also have a role in neurological and metabolic disorders. We recently reported that RFX7 responds to p53 signaling and cellular stress. Furthermore, we found RFX7 target genes to be dysregulated in numerous cancer types also beyond the hematological system. However, our understanding of RFX7's target gene network and its role in health and disease remains limited. Here, we generated RFX7 knock-out cells and employed a multi-omics approach integrating transcriptome, cistrome, and proteome data to obtain a more comprehensive picture of RFX7 targets. We identify novel target genes linked to RFX7's tumor suppressor function and underscoring its potential role in neurological disorders. Importantly, our data reveal RFX7 as a mechanistic link that enables the activation of these genes in response to p53 signaling.

Identifiants

pubmed: 36864036
doi: 10.1038/s41420-023-01378-1
pii: 10.1038/s41420-023-01378-1
pmc: PMC9981735
doi:

Types de publication

Journal Article

Langues

eng

Pagination

80

Subventions

Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : FI 1993/2-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : HO 5281/7-1
Organisme : Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research)
ID : 031L016D

Informations de copyright

© 2023. The Author(s).

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Auteurs

Katjana Schwab (K)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Luis Coronel (L)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Konstantin Riege (K)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Erika K Sacramento (EK)

Core Facility for Proteomics, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Norman Rahnis (N)

Core Facility for Proteomics, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

David Häckes (D)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Emilio Cirri (E)

Core Facility for Proteomics, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Marco Groth (M)

Core Facility for Next-Generation Sequencing, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Steve Hoffmann (S)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany.

Martin Fischer (M)

Computational Biology Group, Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Beutenbergstraße 11, 07745, Jena, Germany. Martin.Fischer@leibniz-fli.de.

Classifications MeSH