Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms

Abstract

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

Fig. 1

Outline of the systems medicine rationale. The diagram represents the transformation of diverse prior knowledge and newly generated data into hypotheses using computational and mathematical methods, tools and approaches appropriate for each step

Fig. 2

A fragment of the Parkinson’s disease map. a A disease map repository allows visual multiscale exploration of the contextualised, disease-relevant mechanisms. Here, the contents of the Parkinson’s Disease map can be explored across scales from the neuronal environment to the detailed molecular pathobiology of the disease. Higher scales emerge from the underlying mechanistic descriptions. b Mechanistic details of dopamine metabolism in the dopaminergic neurones are shown. The involvement of Parkinson’s disease familial genes, PARK2 (Parkin), PARK7 (DJ-1) and SNCA (alpha-synuclein) is highlighted. Excessive oxidation of dopamine into dopamine quinone may lead to conformational changes in proteins and subsequently result in molecular neuropathology associated with Parkinson’s, marked as “disease endpoints” in the figure

Fig. 3

The Disease Maps Project as a community of communities. a A collaboration for building one disease map. b Disease expert groups. c Pathway expert groups. Light colours: computational biology groups. Solid colours: domain experts. The Disease Maps hub is to be used for sharing experience, improving best practices and agreed-upon protocols, exchanging reusable biological processes and pathway modules. It is also an effort to create an infrastructure and set of tools to help each project to progress faster

Fig. 4

Representation of biological networks. Note that in all the four cases (a−d) the same set of proteins is shown but the relationships are represented differently. The disease maps employ the two sequential representations: process descriptions and activity flows. These representations correspond to the Process Description and Activity Flow languages of the SBGN standard (adapted from ref. )

Fig. 5

Sensitivity of TNBC cell lines to combination of DNA repair inhibitors. a Correlation analysis of survival to DT01 and Olaparib in TNBC and control cell lines. b Molecular portraits of DT01 and Olaparib-sensitive/resistant TNBC cell lines visualised on DNA repair map. c Cell survival to combination of DT01 and Olaparib. With DT01 (black line), without DT01 (grey line), dashed lines indicate calculated cell survival for additive effect of two drugs (adapted from Jdey et al.)

Fig. 6

Prediction of synthetic interaction combination to achieve invasive phenotype in colon cancer mice model. a Comprehensive signalling network of EMT regulation, a part of the ACSN. b Scheme representing major players regulating EMT after structural analysis and reduction of signalling network complexity. c Simplistic model of EMT regulation. d Simplistic model explaining regulation involving Notch, p53 and Wnt pathways. e Lineage tracing of cell in tumour and in distant organs and immunostaining for major EMT markers. f Regulation of p53, Notch and Wnt pathways in invasive colon cancer in human (TCGA data) (adapted from Chanrion et al.)