Alzheimer’s disease (AD) and Parkinson’s disease (PD) are caused by progressive degeneration and/or death of nerve cells. In AD, the patient’s memory and ability to think and carry out tasks is slowly destroyed; while PD mainly affects the patient's physical abilities: patients lose body control and have difficulty with movement and coordination1
. Both diseases are strongly linked with the process of aging3
. For most patients with AD and PD, symptoms first appear after age 60. Since the average age of the population is increasing, the numbers of AD and PD patients are expected to grow rapidly: e.g., it is estimated that 5.4 million Americans have AD in 2010 and this number will increase to 11–16 million in 20505
To date, FDA has approved several drugs to slow the progression of AD and PD. Most drugs attempt to prevent the breakdown of critical chemicals whose levels are decreased in patients, e.g., cholinesterase inhibitors for AD patients slow the metabolic breakdown of acetylcholine that is involved in nerve cell communication; carbidopa for PD delays the conversion of levodopa. However, almost all of the currently available drugs are effective for short periods (a few months to a few years). For both diseases, in order to develop more efficient treatments and drugs, it is important to investigate and understand the molecular mechanisms and molecular networks that are altered in the development and progression of diseases.
A large number of studies, including recent genome-wide associate studies (GWAS)6
, traditional association, linkage, and gene expression studies, have been conducted to identify genes associated with AD and PD incidence and progression. Consequently, hundreds of potentially associated genes have been identified. A few of these genes have very strong connections with disease. In particular, APOE is linked to approximately 50% of AD patients12
, and alpha-synuclein is associated with PD in members of a large Italian family13
. However, for both diseases, the majority of the hundreds of identified genes are likely to individually have both small and potentially complex effects on the development and progression of disease14
. To uncover the underlying molecular mechanisms in these two diseases, comprehensive analysis of identified genes and their interactions within a network framework might provide many important insights beyond the traditional single-gene or single-marker analyses16
Network and pathway analysis are relatively new approaches to the study and identification of dysregulated components in diseases18
. The underlying principle is that human diseases are caused by perturbations of the complex networks/pathways that link molecular components (such as genes and proteins) in a human cell. Pathways emphasize what is known and relatively well understood, thus the results can be easily integrated into familiar biological frameworks. On the other hand, current knowledge of pathways is incomplete, and network analysis explores new connections, and connects what are often perceived as distinct pathways. Network analysis has been employed to detect the networks associated with many complex diseases, such as cancer19
. Recently, we have used integrated genome wide mRNA expression with protein-protein interaction (PPI) networks to detect sub-networks that are dysregulated in colon cancer and sleep disorders22
. The aim of such studies is to identify the functionally related genes that exhibit coordinate differential expression between healthy and diseased patients. Due to the computational complexity of examining the actions of multiple genes simultaneously, such studies are currently focused on small subnetworks as markers of disease. However, multiple studies have demonstrated that the results are biologically meaningful and can provide testable hypothesis for further validation19
. The framework of PPI permits scoring of multiple gene combinations within a highly functional context, while the reduced search space limits multiple testing corrections. These PPI sub-networks can reveal critical nodes and edges reflecting both biomarkers of disease, e.g. molecular beacons of the condition, as well as pinpoint critical nodes for potential functional intervention, e.g. important drug targets. We wished to test these properties of molecular network assessment in the context of known information on AD and PD, including the known biomarkers and known targets of drug treatment.
Although AD and PD have their own unique neuropathological features; many patients with one disease later develop symptoms of the other24
. This observation suggests the presence of common genetic variants that predispose individuals to both diseases and/or age related similarities in disease progression. Furthermore, beyond common genetic variants, perturbation of common pathways or network connections may also be shared in AD and PD. Studies show that the cerebral accumulation of beta-amyloid is associated with AD while alpha-synuclein is linked with PD29
. Using a transgenic mouse model and NMR spectroscopy, a recent study has suggested an interaction between beta-amyloid and alpha-synuclein31
providing a potential molecular connection between AD and PD. Motivated by these observations, we conducted a systematic comparative analysis to investigate the molecular mechanisms and relationship of AD and PD, by taking advantage of the large amount of available molecular data for both diseases. We identified genes and networks strongly associated with AD and PD, and comparative analysis showed that AD and PD have strong connections and shared components at both molecular and network levels.