Systems biological approach on neurological disorders: a novel molecular connectivity to aging and psychiatric diseases

Abstract

Background: Systems biological approach of molecular connectivity map has reached to a great interest to understand the gene functional similarities between the diseases. In this study, we developed a computational framework to build molecular connectivity maps by integrating mutated and differentially expressed genes of neurological and psychiatric diseases to determine its relationship with aging.

Results: The systematic large-scale analyses of 124 human diseases create three classes of molecular connectivity maps. First, molecular interaction of disease protein network generates 3632 proteins with 6172 interactions, which determines the common genes/proteins between diseases. Second, Disease-disease network includes 4845 positively scored disease-disease relationships. The comparison of these disease-disease pairs with Medical Subject Headings (MeSH) classification tree suggests 25% of the disease-disease pairs were in same disease area. The remaining can be a novel disease-disease relationship based on gene/protein similarity. Inclusion of aging genes set showed 79 neurological and 20 psychiatric diseases have the strong association with aging. Third and lastly, a curated disease biomarker network was created by relating the proteins/genes in specific disease contexts, such analysis showed 73 markers for 24 diseases. Further, the overall quality of the results was achieved by a series of statistical methods, to avoid insignificant data in biological networks.

Conclusions: This study improves the understanding of the complex interactions that occur between neurological and psychiatric diseases with aging, which lead to determine the diagnostic markers. Also, the disease-disease association results could be helpful to determine the symptom relationships between neurological and psychiatric diseases. Together, our study presents many research opportunities in post-genomic biomarkers development.

Figure 1

Computational framework for developing molecular connectivity maps. The framework consists of three major components: disease protein network, disease-disease network and disease biomarker network. The first component takes the inputs from database and literature and outputs a disease protein network (DPN). The second component takes the input from DPN and generates the output of positively scored disease-disease network (DDN) using scoring algorithm. Further, the second component was used to generate sub-component of common pathway network (CPN). The final disease biomarker network (DBN) component was generated from DPN showing proteins specific to diseases.

Figure 2

Disease protein network (DPN). In DPN each nodes (seed and enriched proteins) were colored yellow and the aging genes were colored as red and the proteins interactions were represented in violet solid lines.

Figure 3

Disease-disease network (DDN). In disease-disease network, each node represents to a disease yellow colored. Two diseases were connected by red solid line, if they attained the positive score in algorithm. The total of 4845 positively scored disease-disease interactions were shown along with the aging interactions.

Figure 4

Common pathway network (CPN). In CPN, node represents to a disease (gray) and their associated pathway represented in red. Two diseases were connected to a pathway, if both the disease shares proteins/genes that are associated to a pathway.

Figure 5

Disease biomarker network (DBN). The disease biomarker network contains 24 diseases (green) with 73 biomarkers. The biomarkers were colored based on the diagnostic parameters (gray). The associations of biomarkers with any of the diagnosis parameters (gray) are represented in yellow, while other biomarkers are indicated in violet.

Figure 6

Characterizing the disease modules. (a) Histogram of the index of aggregation distribution for Parkinson's disease enriched sets of proteins randomly selected from a database. The arrow indicates the aggregation values for the enriched Parkinson's disease proteins set. The Venn diagram (b) showed the neurological diseases relationships between aging and psychiatric diseases, The Venn diagram (c) showing the neurological disease relationship with depression. (d) Peak representation of positively scored disease pairs category and MeSH disease pairs category. The common region indicates the similarity disease pairs between the two categories. The Venn diagram (e) shows the presences of biomarkers in biofluid and house keeping genes.

Figure 7

MeSH based disease classification of 124 diseases. The manually collected 124 diseases represented in white blocks were grouped based on the MeSH disease category (blue block) of neurological and psychiatric diseases (yellow block). Most of the diseases were linked to one or more MeSH disease categories. The overall linkage between the diseases was represented by solid lines.