Cell Death and DifferentiationCancer BiologyPeer-reviewed journal article
Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network
Anthony M. Boutelle, Aicha R. Mabene, David YaoiD, Haiqing XuiD, Mengxiong WangiD, Yuning J. TangiD, Steven S. LopeziD, Sauradeep SinhaiD, János DemeteriD, Ran Cheng, Brooks A. BenardiD, Edel M. McCrea, Liz J. ValenteiD, Alexandros P. DrainasiD, Martin FischeriD, Ravindra MajetiiD, Dmitri A. PetroviD, Peter K. JacksoniD, Fan YangiD, Monte M. WinslowiD, Michael Cory BassikiD, Laura Donatella AttardiiD
Abstract
TP53, the most frequently mutated gene in human cancer, encodes a transcriptional activator that induces myriad downstream target genes. Despite the importance of p53 in tumor suppression, the specific p53 target genes important for tumor suppression remain unclear. Recent studies have identified the p53-inducible gene Zmat3 as a critical effector of tumor suppression, but many questions remain regarding its p53-dependence, activity across contexts, and mechanism of tumor suppression alone and in cooperation with other p53-inducible genes. To address these questions, we used Tuba-seqUltra somatic genome editing and tumor barcoding in a mouse lung adenocarcinoma model, combinatorial in vivo CRISPR/Cas9 screens, meta-analyses of gene expression and Cancer Dependency Map data, and integrative RNA-sequencing and shotgun proteomic analyses. We established Zmat3 as a core component of p53-mediated tumor suppression and identified Cdkn1a as the most potent cooperating p53-induced gene in tumor suppression. We discovered that ZMAT3/CDKN1A serve as near-universal effectors of p53-mediated tumor suppression that regulate cell division, migration, and extracellular matrix organization. Accordingly, combined Zmat3-Cdkn1a inactivation dramatically enhanced cell proliferation and migration compared to controls, akin to p53 inactivation. Together, our findings place ZMAT3 and CDKN1A as hubs of a p53-induced gene program that opposes tumorigenesis across various cellular and genetic contexts.
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 10
- FWCI
- 4.25
- Field percentile
- top 5%
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