Abstract
BackgroundDeregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context.ResultsWe analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'.ConclusionsUsing JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.
Highlights
Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions
We show that joint deregulation analysis (JODA) performs better than investigating gene regulation in each cell population separately: with the deregulation scores, JODA prioritizes genes that are more enriched in those Gene Ontology (GO [26]) terms which are important for the switch between the compared cell populations
Deregulation can be explained by a hierarchy of direct transcription factor (TF)-DNA binding events we investigate the hierarchy of direct regulatory relations, which could explain the effect of the ATM pathway on the deregulated target genes
Summary
Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Molecular profiling of cells sampled from healthy patients and patients suffering from diseases led to the discovery of signatures of deregulated genes, i.e., distinctive expression patterns of genes that are differentially regulated and change expression between these two populations of cells. Such deregulated genes facilitate classification into different tumors [1,2,3,4,5], define new cancer subtypes and can serve as predictors of tumor differentiation stages and patient survival [6,7,8,9,10]. Many methods have been developed to infer differential interactions from gene expression data, either based on linear measures of correlation [20,21,14,22] and regression [23] or non-linear information theoretic criteria [13].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
