Background. Schizophrenia is a neurodevelopmental disorder affecting approximately 1% of the population. It is particularly debilitating, with indicators including auditory hallucinations, delusions, disorganized and unusual thinking and speech, and social isolation due to impairment in social cognition, paranoia, and general avolition (i.e., apathy or lack of motivation). The age of onset is normally 25 in females and 18 in males, but it can occur at younger ages in rare instances, which allows for monitoring of the disease during formative years of brain development. These schizophrenic children share symptoms with their adult counterparts as well as with children who suffer from autism and other pervasive developmental disabilities.
Objectives. To date, several putative schizophrenia susceptibility genes have been identified, but the complex nature of the disease complicates the overall picture. Systems-level analysis and the identification of core molecular pathways are still needed. In addition, a comparison of the genes and pathways affected by schizophrenia and autism may yield new insight into neurological diseases as a whole.
Methods. This study analyzed Illumina microarray expression data from 86 childhood-onset schizophrenia cases and 72 healthy siblings. A mixed-model ANOVA was run to eliminate confounding factors such as age, sex, race, and array and scan date variables. Subsequently, cases and controls were grouped in different ways and 2-class t-tests with standard Bonferroni correction revealed sets of genes that significantly differentiated the schizophrenia patients from healthy individuals. These genes were analyzed using Ingenuity Pathway Analysis and Ariadne Pathway Studio 5 network prediction software to reveal relationships between the differentially expressed genes.
Results. In the first analysis, significant differentially expressed genes were identified between the cases and controls; some had been previously associated with SZ but the vast majority of genes were novel. Implicated pathways reflected neurological and sugar metabolism functions as major themes. Secondly, principal components analysis and hierarchical clustering of the schizophrenic patients based on significant differential gene expression levels led to the discovery of three distinct groups, possibly correlating with disease phenotypes or severity of specific symptoms. The group with the most differentially expressed genes relative to controls was examined further. A third analysis which investigated the gene expression profiles of schizophrenia patients relative to their respective siblings revealed detrimental impact on the nervous system and sugar-processing. Interestingly, but not surprisingly, roughly 20% of the significant differentially expressed genes in analyses 1 and 2 (68 out of 313; 280 out of 1554) overlap with differentially expressed genes between autism case-controls identified independently in our laboratory.
Conclusions. This study uncovers several new potential schizophrenia susceptibility genes and lends insight into pathways that may be affected in abnormal development leading to schizophrenia and other neurological diseases. It also uses these detected pathways to explain the co-morbidity of diabetes and epilepsy seen in previous studies in the literature. Furthermore, it suggests a possible way to classify biological subtypes of schizophrenia on the basis of gene expression profiles.
|Advisor:||Hu, Valerie W.|
|Commitee:||Vanderhoek, Jack Y.|
|School:||The George Washington University|
|Department:||Genomics and Bioinformatics|
|School Location:||United States -- District of Columbia|
|Source:||MAI 47/06M, Masters Abstracts International|
|Keywords:||Autism, Childhood-onset schizophrenia, Expression profile, Gene, Microarray, Schizophrenia|
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