| Abstract No.: | C-C3084 |
| Country: | Canada |
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| Title: | DISTURBED CELL ADHESION MOLECULES EXPRESSION IN SUICIDE BRAINS |
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| Authors/Affiliations: | 2 Vladimir Zhurov*; 3 Miklos Palkovits; 4 Gabor Faludi; 5 Zul Merali; 1 Hymie Anisman; 2 Michael Poulter;
1 Carleton University, Ottawa, ON, Canada; 2 Robarts Research Institute, London, ON, Canada; 3 Semmelweis University, Budapest, Hungary; 4 Semmelweis University Hospital, Budapest, Hungary; 5 University of Ottawa, ON, Canada
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| Content: | Suicide is among the leading cause of death in the adults between 18 and 35 and depression is the leading cause of disability and the 4-th leading contributor to the global burden of disease worldwide. Approximately 16% of population suffer from depression at least once during a lifespan with the mean age of onset being late 20's.
A number of genome-wide microarray analysis of depressed suicide has been reported in recent years. However, these studies were primarily focused on discovery of differential expression of individual genes previously described within neurobiological context, rather than attempting to analyze alterations of gene expression within larger genetic networks or molecular pathways.
In the present study we implemented a method of analysis of gene expression data which was based on building and displaying the relationships between biological molecules and processes. We present evidence that extracellular matrix (ECM) and cell adhesion and signaling molecules and pathways may be implicated in neurobiology and development of major depressive disorder and suicidality.
We analyzed gene expression in brain samples corresponding to Broadmann area 9 from depressed suicide victims (16) and non-psychiatric control subjects (8) using GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix, Santa Clara, California). Probe level expression data was generated according to PLIER-16 algorithm. Partek Genomics Suite (Partek, St. Louis, Missouri) was used to determine differentially expressed genes between depressed suicides and non-psychiatric controls using Principal Component Analysis (PCA) and Analysis of Covariance (ANCOVA). We have used subjects age, brain pH, post mortem interval (PMI), RNA Integrity Number (RIN) and experimental batch as covariate factors in ANCOVA. Genes which demonstrated significantly different expression levels between classes at p ≤ 0.01 with Fold Change (FC) ≥ 1.5 in either direction and which expression levels were not significantly correlated with covariate factors (p > 0.01) were considered for subsequent gene network analysis. List of 248 probe sets representing 210 known genes obtained by filtering of expression data according to criteria described above were analyzed using Pathway Studio software version 5.0 (Ariadne Genomics, Rockville, Maryland).
Initial pathway analysis of 210 differentially expressed genes generated network of 39 genes with known direct relationships. Functional annotation and enrichment analysis demonstrated that ECM and cell-cell interaction proteins were overrepresented in both gene sets.
To further analyze differentially expressed genes with known relationships we generated gene networks based on Pearson's correlation for both classes using Pathway Studio software. In control class expression of 22 of 39 genes was positively correlated. In depressed suicide class correlation of gene expression was increased with 36 of 39 genes demonstrating concerted expression. However, only 6 correlation relationships overlap between classes.
Our results show a concerted upregulation of expression of genes involved in ECM, membrane signal transduction and cell-cell interaction. Our results demonstrate that analysis of molecular pathways and gene networks is a viable approach to analysis of microarray data in depressed suicide studies. |
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