A network of epigenetic modifiers and DNA repair genes controls tissue-specific copy number alteration preference
Abstract
Copy number alterations (CNAs) in cancer patients show a large variability in their number, length and position. CNA number and length are linked to patient survival suggesting clinical relevance. However, the sources of this variability are not known. We have identified genes that tend to be mutated in samples having few or many CNAs, which we term CONIM genes (COpy Number Instability Modulators). CONIM proteins cluster into a densely connected subnetwork of physical interactions and many of them are epigenetic modifiers. Therefore, we investigate how the epigenome of the tissue-of-origin influences the position of CNA breakpoints and the properties of the resulting CNAs. We find that the presence of heterochromatin in the tissue-of-origin contributes to the recurrence and length of CNAs in the respective cancer type.
Data availability
-
HIPPIE protein-protein interactionsPublicly available at the Human Integrated Protein-Protein Interaction rEference website.
-
Chromatin state model (18 states)Publicly available at NIH Roadmap Epigenomics Mapping Consortium.
-
Histone ChIP-seq peaksPublicly available at NIH Roadmap Epigenomics Mapping Consortium.
-
RNA expression (RNAseq)Publicly available at NIH Roadmap Epigenomics Mapping Consortium.
-
SNP6 focal copy altered segmentsPublicly available at the Broad Institute website.
-
Illumina HiSeq copy number dataPublicly available at the Broad Institute website.
-
SNP6 recurrent copy number alterations (GISTIC2)Publicly available at the Broad Institute website.
-
Mutation dataPublicly available at the Broad Institute website.
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (SCHAÂ 1933/1-1)
- Martin H Schaefer
European Commission (HEALTH-F4-2011-278568)
- Luis Serrano
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2016, Cramer 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
-
- 1,785
- views
-
- 366
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
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
-
- Cell Biology
- Computational and Systems Biology
Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.
-
- Computational and Systems Biology
- Structural Biology and Molecular Biophysics
Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.