title: Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression.
abstract: BACKGROUND
  Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.
  
  
  RESULTS
  This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.
  
  
  CONCLUSIONS
  Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.
authors.affiliation: Department of Information Management, Kainan University, Taoyuan, Taiwan.
authors.forename: Sheng-An
authors.initials: SA
authors.lastname: Lee
authors.forename: Theresa Tsun-Hui
authors.initials: TT
authors.lastname: Tsao
authors.forename: Ko-Chun
authors.initials: KC
authors.lastname: Yang
authors.forename: Han
authors.initials: H
authors.lastname: Lin
authors.forename: Yu-Lun
authors.initials: YL
authors.lastname: Kuo
authors.forename: Chien-Hsiang
authors.initials: CH
authors.lastname: Hsu
authors.forename: Wen-Kuei
authors.initials: WK
authors.lastname: Lee
authors.forename: Kuo-Chuan
authors.initials: KC
authors.lastname: Huang
authors.forename: Cheng-Yan
authors.initials: CY
authors.lastname: Kao
mesh_terms: D001714:Bipolar Disorder / Q000235:genetics / Q000378:metabolism*; D003865:Major Depressive Disorder / Q000235:genetics / Q000378:metabolism*; D020869:Gene Expression Profiling; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D017397:Prefrontal Cortex / Q000378:metabolism*; D060066:Protein Interaction Maps; D040901:Proteomics; D012559:Schizophrenia / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction