Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients

BackgroundGene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions.MethodsIn this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules.ResultsWe obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology.ConclusionsThrough our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.

[1]  Walter J. Lukiw,et al.  Analysis of Network Based Co-Expression Modules for Alzheimer's Disease , 2013, MedInfo.

[2]  L. Mucke,et al.  Alzheimer Mechanisms and Therapeutic Strategies , 2012, Cell.

[3]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[4]  I. Mook‐Jung,et al.  Disruption of blood-brain barrier in Alzheimer disease pathogenesis , 2013, Tissue barriers.

[5]  Hirohisa Tajima,et al.  Neuronal cell death in Alzheimer's disease and a neuroprotective factor, humanin. , 2006, Current neuropharmacology.

[6]  P. Mcgeer,et al.  The inflammatory response system of brain: implications for therapy of Alzheimer and other neurodegenerative diseases , 1995, Brain Research Reviews.

[7]  Wei Kong,et al.  Construction of Transcriptional Regulatory Network of Alzheimer’s Disease Based on PANDA Algorithm , 2018, Interdisciplinary Sciences: Computational Life Sciences.

[8]  Lin Li,et al.  Suppression of USP18 Potentiates the Anti-HBV Activity of Interferon Alpha in HepG2.2.15 Cells via JAK/STAT Signaling , 2016, PloS one.

[9]  D. Stephan,et al.  Genetic control of human brain transcript expression in Alzheimer disease. , 2009, American journal of human genetics.

[10]  Eric M Reiman,et al.  Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. , 2007, Physiological genomics.

[11]  G. McFadden,et al.  SAMD9 Is an Innate Antiviral Host Factor with Stress Response Properties That Can Be Antagonized by Poxviruses , 2014, Journal of Virology.

[12]  Kun Huang,et al.  Normalized lmQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers , 2014, Cancer informatics.

[13]  D. Selkoe.  Presenilin, Notch, and the genesis and treatment of Alzheimer's disease , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[14]  L. Tran,et al.  Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease , 2013, Cell.

[15]  R. Yu,et al.  Ganglioside-Dependent Neural Stem Cell Proliferation in Alzheimer’s Disease Model Mice , 2015, ASN neuro.

[16]  Jie Zhang,et al.  A matrix rank based concordance index for evaluating and detecting conditional specific co-expressed gene modules , 2016, BMC Genomics.

[17]  Burkhard Becher,et al.  Immune attack: the role of inflammation in Alzheimer disease , 2015, Nature Reviews Neuroscience.

[18]  Ming Yan,et al.  Role of ISG15 protease UBP43 (USP18) in innate immunity to viral infection , 2004, Nature Medicine.

[19]  Matko Bosnjak,et al.  REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms , 2011, PloS one.

[20]  R. Tanzi,et al.  Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease , 2016, Science Translational Medicine.

[21]  G. Davı̀,et al.  Determinants of platelet activation in Alzheimer's disease , 2007, Neurobiology of Aging.

[22]  A. Suzumura,et al.  Sweepers in the CNS: Microglial Migration and Phagocytosis in the Alzheimer Disease Pathogenesis , 2012, International journal of Alzheimer's disease.

[23]  Jie Zhang,et al.  Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients , 2016, BMC Genomics.

[24]  A. Saykin,et al.  Association of cancer history with Alzheimer's disease onset and structural brain changes , 2014, Front. Physiol..

[25]  Hartwig Wolburg,et al.  Tight junctions of the blood-brain barrier: development, composition and regulation. , 2002, Vascular pharmacology.

[26]  D. Geschwind,et al.  A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging , 2008, The Journal of Neuroscience.

[27]  Carl W. Cotman,et al.  Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer's disease , 2013, Neurobiology of Aging.

[28]  Berislav V. Zlokovic,et al.  Apolipoprotein E controls cerebrovascular integrity via cyclophilin A , 2012, Nature.

[29]  L. James,et al.  TRIM21 Promotes cGAS and RIG-I Sensing of Viral Genomes during Infection by Antibody-Opsonized Virus , 2015, PLoS pathogens.

[30]  M. Beal,et al.  Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases , 2006, Nature.

[31]  J. Dennis,et al.  Mitochondrial Dysfunction in Alzheimer’s Disease and the Rationale for Bioenergetics Based Therapies , 2016, Aging and disease.

[32]  Yang Xiang,et al.  Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability , 2012, PLoS Comput. Biol..

[33]  D. Shoemark,et al.  16S rRNA Next Generation Sequencing Analysis Shows Bacteria in Alzheimer’s Post-Mortem Brain , 2017, Front. Aging Neurosci..

[34]  J. Hardy,et al.  The Amyloid Hypothesis of Alzheimer ’ s Disease : Progress and Problems on the Road to Therapeutics , 2009 .

[35]  K Kosaka,et al.  Brain site‐specific gene expression analysis in Alzheimer's disease patients , 2006, European journal of clinical investigation.

[36]  A. G. de la Fuente.  From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. , 2010, Trends in genetics : TIG.

[37]  Jie Zhang,et al.  Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers , 2017, BMC Genomics.

[38]  I. Grundke‐Iqbal,et al.  Role of glycosylation in hyperphosphorylation of tau in Alzheimer's disease , 2002, FEBS letters.

[39]  E. Emmott,et al.  IFIT3 and IFIT2/3 promote IFIT1-mediated translation inhibition by enhancing binding to non-self RNA , 2018, bioRxiv.

[40]  C. Brayne,et al.  Alterations in the blood brain barrier in ageing cerebral cortex in relationship to Alzheimer-type pathology: A study in the MRC-CFAS population neuropathology cohort , 2011, Neuroscience Letters.

[41]  H. May,et al.  Incidence of Dementia in Relation to Genetic Variants at PITX2, ZFHX3, and ApoE ε4 in Atrial Fibrillation Patients , 2015, Pacing and clinical electrophysiology : PACE.

[42]  A. Fuente,et al.  From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases , 2010 .

[43]  M. Diamond,et al.  The broad-spectrum antiviral functions of IFIT and IFITM proteins , 2012, Nature Reviews Immunology.

[44]  Avi Ma'ayan,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[45]  Wenyi Wei,et al.  Functional analyses of major cancer-related signaling pathways in Alzheimer's disease etiology. , 2017, Biochimica et biophysica acta. Reviews on cancer.

[46]  David M. Rocke,et al.  Differential Network Analyses of Alzheimer's Disease Identify Early Events in Alzheimer's Disease Pathology , 2014, International journal of Alzheimer's disease.

[47]  P. Carvey,et al.  Blood–Brain Barrier Pathology in Alzheimer's and Parkinson's Disease: Implications for Drug Therapy , 2007, Cell transplantation.

[48]  Ruoming Jin,et al.  Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia , 2010, BMC Bioinformatics.

[49]  C. Lendon,et al.  A common biological mechanism in cancer and Alzheimer's disease? , 2009, Current Alzheimer research.