Preservation affinity in consensus modules among stages of HIV-1 progression

BackgroundAnalysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding.MethodsHIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach.ResultsWe discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages.ConclusionsWe have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages.

[1]  Ujjwal Maulik,et al.  A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions , 2012, PloS one.

[2]  J. Church Identification of Host Proteins Required for HIV Infection Through a Functional Genomic Screen , 2008, Pediatrics.

[3]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[4]  S. Horvath,et al.  Conservation and evolution of gene coexpression networks in human and chimpanzee brains , 2006, Proceedings of the National Academy of Sciences.

[5]  P. Mercié,et al.  [AIDS--the first 20 years]. , 2001, La Revue de medecine interne.

[6]  Douglas D. Richman,et al.  Differential gene expression in HIV-infected individuals following ART. , 2013, Antiviral research.

[7]  Mirjam Kretzschmar,et al.  Modern Infectious Disease Epidemiology , 2010 .

[8]  Wei-Min Liu,et al.  Analysis of high density expression microarrays with signed-rank call algorithms , 2002, Bioinform..

[9]  Elhanan Borenstein,et al.  Broker Genes in Human Disease , 2010, Genome biology and evolution.

[10]  P. Selwyn,et al.  Diagnosis and initial management of acute HIV infection. , 2010, American family physician.

[11]  Sumanta Ray,et al.  Discovering preservation pattern from co-expression modules in progression of HIV-1 disease: An eigengene based approach , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[12]  J. Margolick,et al.  Studies in subjects with long-term nonprogressive human immunodeficiency virus infection. , 1995, The New England journal of medicine.

[13]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[14]  Ujjwal Maulik,et al.  Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach , 2014, BMC Bioinformatics.

[15]  Luc Montagnier,et al.  The discovery of HIV as the cause of AIDS. , 2003, The New England journal of medicine.

[16]  Eyke Hüllermeier,et al.  Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction , 2013, Bioinform..

[17]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[18]  Peter Langfelder,et al.  When Is Hub Gene Selection Better than Standard Meta-Analysis? , 2013, PloS one.

[19]  J. Lieberman,et al.  Identification of Host Proteins Required for HIV Infection Through a Functional Genomic Screen , 2007, Science.

[20]  L. Furlong Human diseases through the lens of network biology. , 2013, Trends in genetics : TIG.

[21]  Ujjwal Maulik,et al.  A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions , 2015, Briefings Bioinform..

[22]  Peter Langfelder,et al.  Is human blood a good surrogate for brain tissue in transcriptional studies? , 2010, BMC Genomics.

[23]  Karin Breuer,et al.  InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation , 2012, Nucleic Acids Res..

[24]  S. Horvath,et al.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks , 2006, BMC Genomics.

[25]  Homin K. Lee,et al.  Coexpression analysis of human genes across many microarray data sets. , 2004, Genome research.

[26]  Martin Meier-Schellersheim,et al.  Pathogenesis of HIV infection: what the virus spares is as important as what it destroys , 2006, Nature Medicine.

[27]  John E. Bennett,et al.  Mandell, Douglas, and Bennett's principles and practice of infectious diseases vol.1 , 2012 .

[28]  G. Morris,et al.  Toxic Dusts, Fumes and Gases in Industry , 1943 .

[29]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[30]  Jun Dong,et al.  Understanding network concepts in modules , 2007, BMC Systems Biology.

[31]  Matej Oresic,et al.  Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process , 2007, Bioinform..

[32]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Ph.D. Joseph Heitman M.D.,et al.  Mandell, Douglas, and Bennett's Principles and Practice of Infectious Diseases , 2004, Mycopathologia.

[34]  Sanghamitra Bandyopadhyay,et al.  Discovering Condition Specific Topological Pattern Changes in Coexpression Network: An Application to HIV-1 Progression , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[35]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[36]  Steve Horvath,et al.  Network neighborhood analysis with the multi-node topological overlap measure , 2007, Bioinform..

[37]  A. Fauci,et al.  The immunopathogenesis of human immunodeficiency virus infection. , 1993, The New England journal of medicine.

[38]  Mirjam Kretzschmar,et al.  Modern infectious disease epidemiology : concepts, methods, mathematical models, and public health , 2010 .

[39]  Christian Brander,et al.  Virological, Immune and Host genetics Markers in the Control of HIV Infection , 2009, Disease markers.

[40]  Andy M. Yip,et al.  Gene network interconnectedness and the generalized topological overlap measure , 2007, BMC Bioinformatics.

[41]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[42]  Eyke Hüllermeier,et al.  Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification , 2016, BioData Mining.

[43]  Donna R. Maglott,et al.  Human immunodeficiency virus type 1, human protein interaction database at NCBI , 2008, Nucleic Acids Res..

[44]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[45]  R. König,et al.  Global Analysis of Host-Pathogen Interactions that Regulate Early-Stage HIV-1 Replication , 2008, Cell.

[46]  Wei Zhao,et al.  Weighted Gene Coexpression Network Analysis: State of the Art , 2010, Journal of biopharmaceutical statistics.

[47]  Matthew D. Dyer,et al.  Supervised learning and prediction of physical interactions between human and HIV proteins. , 2011, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[48]  M. Paiardini,et al.  HIV‐associated chronic immune activation , 2013, Immunological reviews.

[49]  Amy S. Espeseth,et al.  Genome-scale RNAi screen for host factors required for HIV replication. , 2008, Cell host & microbe.

[50]  Shawn M Gomez,et al.  Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens , 2010, Virology Journal.

[51]  Ichiro Takada,et al.  Wnt and PPARγ signaling in osteoblastogenesis and adipogenesis , 2009, Nature Reviews Rheumatology.