Cyclin Kinase-independent role of p21CDKN1A in the promotion of nascent DNA elongation in unstressed cells
Abstract
The levels of the cyclin-dependent kinase (CDK) inhibitor p21 are low in S phase and insufficient to inhibit CDKs. We show here that endogenous p21, instead of being residual, it is functional and necessary to preserve the genomic stability of unstressed cells. p21depletion slows down nascent DNA elongation, triggers permanent replication defects and promotes the instability of hard-to-replicate genomic regions, namely common fragile sites (CFS). The p21's PCNA interacting region (PIR), and not its CDK binding domain, is needed to prevent the replication defects and the genomic instability caused by p21 depletion. The alternative polymerase kappa is accountable for such defects as they were not observed after simultaneous depletion of both p21 and polymerase kappa. Hence, in CDK-independent manner, endogenous p21 prevents a type of genomic instability which is not triggered by endogenous DNA lesions but by a dysregulation in the DNA polymerase choice during genomic DNA synthesis.
Article and author information
Author details
Funding
National Institutes of Health (R03 TW008924)
- Vanesa Gottifredi
Agencia Nacional de Promoción Científica y Tecnológica (PICT-2012-1371)
- Vanesa Gottifredi
Agencia Nacional de Promoción Científica y Tecnológica (PICT-2013-1049)
- Vanesa Gottifredi
Company of Biologists (Travel Fellowship)
- Sabrina F Mansilla
Laboratoire d'Excellence Toulouse Cancer LABEX TOUCAN
- Jean-Sébastien Hoffmann
La Ligue Nationale contra le Cancer
- Jean-Sébastien Hoffmann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Michael R Botchan, University of California, Berkeley, United States
Version history
- Received: May 21, 2016
- Accepted: October 7, 2016
- Accepted Manuscript published: October 14, 2016 (version 1)
- Version of Record published: November 23, 2016 (version 2)
Copyright
© 2016, Mansilla et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,977
- Page views
-
- 503
- Downloads
-
- 30
- Citations
Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.
-
- Cancer Biology
- Computational and Systems Biology
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.