An integrative genomic study implicates the postsynaptic density in the pathogenesis of bipolar disorder

Neuropsychopharmacology

Volumn 41, Issue 3, 2016, Pages 886-895

  Akula, Nirmala ^a   Wendland, Jens R ^a,c   Choi, Kwang H ^b   McMahon, Francis J ^a  

^a NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

^b UNIFORMED SERVICES UNIVERSITY OF THE HEALTH SCIENCES   (United States)

^c PFIZER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POLYADENYLIC ACID; TRANSCRIPTOME;

ARTICLE; AUTOPSY; BIPOLAR DISORDER; BRAIN REGION; COMPARATIVE STUDY; CONTROLLED STUDY; DISEASE ASSOCIATION; DORSOLATERAL PREFRONTAL CORTEX; GENE EXPRESSION; GENE FREQUENCY; GENE REGULATORY NETWORK; GENE SEQUENCE; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENOME ANALYSIS; HUMAN; HUMAN TISSUE; PATHOGENESIS; POSTSYNAPTIC DENSITY; PRIORITY JOURNAL; RNA SEQUENCE; SIGNAL TRANSDUCTION; WEIGHTED GENE COEXPRESSION NETWORK ANALYSIS; GENE EXPRESSION PROFILING; GENETICS; GENOME-WIDE ASSOCIATION STUDY; METABOLISM; MICROARRAY ANALYSIS; PREFRONTAL CORTEX;

BIPOLAR DISORDER; GENE EXPRESSION PROFILING; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MICROARRAY ANALYSIS; POST-SYNAPTIC DENSITY; PREFRONTAL CORTEX; TRANSCRIPTOME;

EID: 84953898329     PISSN: 0893133X     EISSN: 1740634X     Source Type: Journal    
DOI: 10.1038/npp.2015.218     Document Type: Article

References (47)

1. Akula N, Barb J, Jiang X, Wendland JR, Choi KH, Sen SK et al (2014). RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol Psychiatry 19: 1179-1185.
2. Anders S, Huber W (2010). Differential expression analysis for sequence count data. Genome Biol 11: R106.
3. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M et al (2014). An atlas of active enhancers across human cell types and tissues. Nature 507: 455-461.
4. Barry G (2014). Integrating the roles of long and small non-coding RNA in brain function and disease. Mol Psychiatry 19: 410-416.
5. Beneyto M, Meador-Woodruff JH (2008). Lamina-specific abnormalities of NMDA receptor-associated postsynaptic protein transcripts in the prefrontal cortex in schizophrenia and bipolar disorder. Neuropsychopharmacology 33: 2175-2186.
6. Bernard D, Prasanth KV, Tripathi V, Colasse S, Nakamura T, Xuan Z et al (2010). A long nuclear-retained non-coding RNA regulates synaptogenesis by modulating gene expression. EMBO J 29: 3082-3093.
7. Bild AH, Chang JT, Johnson WE, Piccolo SR (2014). A field guide to genomics research. PLoS Biol 12: E1001744.
8. Chen C, Cheng L, Grennan K, Pibiri F, Zhang C, Badner JA et al (2013a). Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry 18: 1308-1314.
9. Chen DT, Jiang X, Akula N, Shugart YY, Wendland JR, Steele CJ et al (2013b). Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol Psychiatry 18: 195-205.
10. Choi KH, Higgs BW, Wendland JR, Song J, McMahon FJ, Webster MJ (2011). Gene expression and genetic variation data implicate PCLO in bipolar disorder. Biol Psychiatry 69: 353-359.
11. Clarke C, Madden SF, Doolan P, Aherne ST, Joyce H, O'Driscoll L et al (2013). Correlating transcriptional networks to breast cancer survival: A large-scale coexpression analysis. Carcinogenesis 34: 2300-2308.
12. Dong J, Horvath S (2007). Understanding network concepts in modules. BMC Syst Biol 1: 24.
13. el-Mallakh RS, Wyatt RJ (1995). The Na,K-ATPase hypothesis for bipolar illness. Biol Psychiatry. 37: 235-244.
14. Feng W, Zhang M (2009). Organization and dynamics of PDZdomain-related supramodules in the postsynaptic density. Nat Rev Neurosci 10: 87-99.
15. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P et al (2014). De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179-184.
16. Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S (2007). Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm Genome 18: 463-472.
17. Gaiteri C, Ding Y, French B, Tseng GC, Sibille E (2014). Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 13: 13-24.
18. Gumieiro EH, Abrahao M, Jahn RS, Segretto H, Alves MT, Nannmark U et al (2010). Platelet-rich plasma in bone repair of irradiated tibiae of Wistar rats. Acta Cir Bras 25: 257-263.
19. He Z, Bammann H, Han D, Xie G, Khaitovich P (2014). Conserved expression of lincRNA during human and macaque prefrontal cortex development and maturation. RNA 20: 1103-1111.
20. Hong S, Chen X, Jin L, Xiong M (2013). Canonical correlation analysis for RNA-seq co-expression networks. Nucleic Acids Res 41: E95.
21. Huang da W, Sherman BT, Lempicki RA (2009a). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1-13.
22. Huang da W, Sherman BT, Lempicki RA (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44-57.
23. Iancu OD, Colville A, Darakjian P, Hitzemann R (2014). Coexpression and cosplicing network approaches for the study of mammalian brain transcriptomes. Int Rev Neurobiol 116: 73-93.
24. Iancu OD, Kawane S, Bottomly D, Searles R, Hitzemann R, McWeeney S (2012). Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28: 1592-1597.
25. Kristiansen LV, Meador-Woodruff JH (2005). Abnormal striatal expression of transcripts encoding NMDA interacting PSD proteins in schizophrenia, bipolar disorder and major depression. Schizophr Res 78: 87-93.
26. Langfelder P, Castellani LW, Zhou Z, Paul E, Davis R, Schadt EE et al (2012). A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models. Biochim Biophys Acta 1821: 435-447.
27. Langfelder P, Horvath S (2007). Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 1: 54.
28. Langfelder P, Horvath S (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 9: 559.
29. Langfelder P, Luo R, Oldham MC, Horvath S (2011). Is my network module preserved and reproducible? PLoS Comput Biol 7: E1001057.
30. Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A et al (2014). Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol 134: 1828-1838.
31. Malki K, Pain O, Du Rietz E, Tosto MG, Paya-Cano J, Sandnabba KN et al (2014). Genes and gene networks implicated in aggression related behaviour. Neurogenetics 15: 255-266.
32. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H et al (2012). Systematic localization of common diseaseassociated variation in regulatory DNA. Science 337: 1190-1195.
33. McGettigan PA (2013). Transcriptomics in the RNA-seq era. Curr Opin Chem Biol 17: 4-11.
34. Muhleisen TW, Leber M, Schulze TG, Strohmaier J, Degenhardt F, Treutlein J et al (2014). Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun 5: 3339.
35. Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium (2015). Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 18: 199-209.
36. Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S et al (2008). Functional organization of the transcriptome in human brain. Nat Neurosci 11: 1271-1282.
37. Pennington K, Beasley CL, Dicker P, Fagan A, English J, Pariante CM et al (2008). Prominent synaptic and metabolic abnormalities revealed by proteomic analysis of the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder. Mol Psychiatry 13: 1102-1117.
38. Poultney CS, Samocha K, Kou Y, Liu L, Walker S, Singh T et al (2014). Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515: 209-215.
39. Psychiatric GWAS Consortium Bipolar Disorder Working Group (2011). Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43: 977-983.
40. Schadt EE (2006). Novel integrative genomics strategies to identify genes for complex traits. Anim Genet 37(Suppl 1): 18-23.
41. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophreniaassociated genetic loci. Nature 511: 421-427.
42. Segre AV, Groop L, Mootha VK, Daly MJ, Altshuler D (2010). Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet 6: Pii: E1001058.
43. Torkamani A, Dean B, Schork NJ, Thomas EA (2010). Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res 20: 403-412.
44. Trapnell C, Pachter L, Salzberg SL (2009). TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics 25: 1105-1111.
45. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S et al (2011). Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474: 380-384.
46. Walsh CA, Morrow EM, Rubenstein JL (2008). Autism and brain development. Cell 135: 396-400.
47. Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O'Keeffe S et al (2014). An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34: 11929-11947.