EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

BackgroundIn biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights.ResultsWe develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network.We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes.ConclusionsWe develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the immediate influence zone of epicenters and provide a summary of dysregulated genes, facilitating quick biological analysis. We demonstrate its efficacy on two datasets with differing characteristics, highlighting its general applicability. We also show that EpiTracer is not sensitive to minor changes in the network. The source code for EpiTracer is provided at Github (https://github.com/narmada26/EpiTracer).

[1]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[2]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[3]  J. A. Bondy,et al.  Graph Theory with Applications , 1978 .

[4]  Eric J. Deeds,et al.  Crosstalk and competition in signaling networks. , 2012, Biophysical journal.

[5]  Andreas Krämer,et al.  Causal analysis approaches in Ingenuity Pathway Analysis , 2013, Bioinform..

[6]  Matheus H. Dias,et al.  Fibroblast Growth Factor 2 Causes G2/M Cell Cycle Arrest in Ras-Driven Tumor Cells through a Src-Dependent Pathway , 2013, PloS one.

[7]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[8]  Yang Xiang,et al.  Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models , 2014, BMC Bioinformatics.

[9]  Mark Gerstein,et al.  Interpretation of Genomic Variants Using a Unified Biological Network Approach , 2013, PLoS Comput. Biol..

[10]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[11]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[12]  Elwood S. Buffa,et al.  Graph Theory with Applications , 1977 .

[13]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[14]  Jing Wang,et al.  WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 , 2013, Nucleic Acids Res..

[15]  Tsviya Olender,et al.  GeneCards Version 3: the human gene integrator , 2010, Database J. Biol. Databases Curation.

[16]  Joo-In Park,et al.  Nek6 is involved in G2/M phase cell cycle arrest through DNA damage-induced phosphorylation , 2008, Cell cycle.

[17]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[18]  M. Kahn,et al.  Genomic and Functional Analysis of the E3 Ligase PARK2 in Glioma. , 2015, Cancer research.

[19]  Pornpimol Charoentong,et al.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks , 2009, Bioinform..

[20]  V. Noé,et al.  Identification of novel Sp1 targets involved in proliferation and cancer by functional genomics. , 2012, Biochemical pharmacology.

[21]  J. Ge,et al.  Silencing of EphA2 inhibits invasion of human gastric cancer SGC-7901 cells in vitro and in vivo. , 2012, Neoplasma.

[22]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[23]  J. Padiadpu,et al.  Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance , 2010, Systems and Synthetic Biology.

[24]  Xinghuo Yu,et al.  Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach , 2014, PloS one.

[25]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[26]  R. Newton,et al.  Roles for the Mitogen-activated Protein Kinase (MAPK) Phosphatase, DUSP1, in Feedback Control of Inflammatory Gene Expression and Repression by Dexamethasone* , 2014, The Journal of Biological Chemistry.