Systems biology is a data driven domain, with rapid generation of data about the individual components such as genes, proteins, chemicals, diseases, cell types and organs. To understand complex biological systems and diseases, we need to bring into context available data to detect relations, pattern and links between the individual components, allowing us to formulate and validate scientific hypotheses. The existing knowledge is distributed over different databases. Disease maps are one such method to integrate knowledge about the disease mechanisms from literature and different databases into a single resource and to add context to the knowledge, by organising it into a structured and organised network. Disease maps integrate and annotate knowledge from different molecular mechanisms and biological pathway relevant to the disease into a computer-readable format and enable visual exploration.
The PD map is a manually curated knowledge repository established to describe molecular mechanisms of PD. It compiles literature-based information on PD into an easy to explore and freely accessible molecular interaction map and offers research-facilitating functionalities such as the overlay of experimental data and the identification of drug targets on the map. The PD map integrates and visualises molecular interactions within a cellular context with a focus on processes associated in PD pathology such as synaptic and mitochondrial dysfunction, α-synuclein pathology, impaired protein degradation, and neuroinflammation. It is also the first freely accessible and manually curated knowledge repository of Parkinson’s Disease. PD Map integrates knowledge about the disease mechanisms from literature and different resources into a single resource and to add context to the knowledge. It integrates knowledge from different levels, molecular, pathway, etc. and enables visual exploration. In addition, the maps are computer readable and annotated. The PD map can be easily explored via an intuitive “Google map” layout also on tablets and smartphones. It offers free access to diverse research-facilitating functionalities: relevant annotation and links to scientific databases, overlay of experimental data, or identification of drug targets and chemical interactions by dedicated interfaces.
How: As a graph. Nodes interacting with each other connected by edges. Each node is annotated by a unique identifier. Localisation of the interaction and nodes are represented as compartments providing the map a hierarchical organisation. With the latest update in Spring 2018, 1447 publications are now curated for the map. 80 new publications and updated over 160 interactions. Postsynaptic area and projections to other brain regions, and the dopaminergic transcription were greatly enriched.
Fujita KA, Ostaszewski M, Matsuoka Y, Ghosh S, Glaab E, Trefois C, Crespo I, Perumal TM, Jurkowski W, Antony PM, Diederich N, Buttini M,
Kodama A, Satagopam VP, Eifes S, Del Sol A, Schneider R, Kitano H, Balling R.
Integrating pathways of Parkinson's disease in a molecular interaction map.
Mol Neurobiol. 2014 Feb;49(1):88-102. doi: 10.1007/s12035-013-8489-4. Review. PubMed PMID: 23832570.
Satagopam V, Gu W, Eifes S, Gawron P, Ostaszewski M, Gebel S, Barbosa-Silva A, Balling R, Schneider R.
Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.
Big Data. 2016 Jun;4(2):97-108. doi: 10.1089/big.2015.0057. PubMed PMID: 27441714.
Here we use a GEO public study GSE7621 [Lesnick et al., 2007]. The study used microarrays to detail the global program of gene expression underlying Parkinson's disease. Substantia nigra tissue from postmortem brain of normal and Parkinson disease patients were used for RNA extraction and hybridization on Aymetrix microarrays: 9 replicates for the controls and 16 replicates for the Parkinson's disease patients were used. Both cohorts included males and females. The heatmap mapworkow was used to retrieve the dierentially expressed genes between control and diseased.
Differential gene expression data comparing postmortem brain tissues from male PD patients versus controls are displayed on the PD map blue representing downregulated and red representing upregulated genes . Overlaying the differentially expressed genes on the PD Map provides context about the pathways and mechanisms these genes are involved in. These may suggest new targets for further investigation towards potential treatments.
Overlaying the differentially expressed genes on the PD Map show perturbations in:
Dopamine secretion and recycling: down-regulation of SLC18A2, RIMS1, SLC6A3
Dopaminergic transcription: down-regulation of RET, TH, ALDH1A1, DDC, SLC6A3, SLC18A2, FOXA2, EN1
Dopamine metabolism: down-regulation of TH, DDC, ALDH1A1 and SLC6A3
Post synaptic terminal processes: up-regulation of GRIA4, down-regulation of SLC6A3, RGS4, ALDH1A1
Autophagy: up-regulation of AMBRA1
Calcium signaling and NEF2L2 Activity: up-regulation of CREBBP
Neuroinflammation: up-regulation of PTGS2, SOCS3 and NCF4
TH, ALDH1A1, SLC6A3, SLC18A2, DDC, RET, EN1, FOXA2 are down-regulated, all are involved in dopaminergic transcription. Down-regulated genes (SLC18A2, RIMS1, SLC6A3) are involved in dopamine secretion and recycling. Down-regulated genes (TH, DDC, ALDH1A1) are involved in dopamine metabolism.