A comprehensive molecular interaction map for rheumatoid arthritis.
BACKGROUND
Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine--the scientific approach to medicine in tight relation with basic science--, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).


METHODS
Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.


CONCLUSIONS
We find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being--at the moment--a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable .xml CellDesigner format at 'www.picb.ac.cn/ClinicalGenomicNTW/temp.html' and 'www.celldesigner.org'.
Wu
Gang
G
Group of Clinical Genomic Networks, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, People's Republic of China.
Zhu
Lisha
L
Dent
Jennifer E
JE
Nardini
Christine
C
D001172:Arthritis, Rheumatoid / Q000378:metabolism*; D019295:Computational Biology; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D025941:Protein Interaction Mapping; D049490:Systems Biology