Analysis of Parkinson's disease pathophysiology using an integrated genomics-bioinformatics approach.

The pathogenesis and pathophysiology of a disease determine how it should be diagnosed and treated. Yet, understanding the cause and mechanisms of progression often requires intensive human efforts, especially for diseases with complex etiology. The latest genomic technology coupled with advanced, large-scale data analysis in the field known as bioinformatics has promised a high-throughput approach that can quickly identify disease-affected genes and pathways by examining tissue samples collected from patients and control subjects. Furthermore, significant biological themes indicative of genomic events can be recognized on the basis of affected genes. However, given identified biological themes, it is not clear how to organize genomic events to arrive at a coherent pathophysiological explanation about the disease. To address this important issue, we have developed an innovative method named "Expression Data Up-Stream Analysis" (EDUSA) that can perform a bioinformatics analysis to identify and rank upstream processes effectively. We applied it to Parkinson's disease (PD) using a genomic data set available at a public data repository known as Gene Expression Omnibus (GEO). In this study, disease-affected genes were identified using GEO2R software, and disease-pertinent processes were identified using EASE software. Then the EDUSA program was used to determine the upstream versus downstream hierarchy of the processes. The results confirmed the current misfolded protein theory about the pathogenesis of PD, and provided new insights as well. Particularly, our program discovered that RNA (ribonucleic acid) metabolism pathology was a potential cause of PD, which in fact, is an emerging theory of neurodegenerative disorders. In addition, it was found that the dysfunction of the transport system seemed to occur in the early phase of neurodegeneration, whereas mitochondrial dysfunction appeared at a later stage. Using this methodology, we have demonstrated how to determine the stages of disease development with single-point data collection.