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1.  Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects 
Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome.
Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling.
Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens.
Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.
doi:10.3389/fcell.2014.00059
PMCID: PMC4220167  PMID: 25414848
rheumatoid arthritis; multi-omic data integration; host-microbiome interface; protein-protein interaction; network topology
2.  Correction: A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis 
PLoS ONE  2010;5(4):10.1371/annotation/f67a90fb-3e4e-4484-bffe-fcfafbfe88c7.
doi:10.1371/annotation/f67a90fb-3e4e-4484-bffe-fcfafbfe88c7
PMCID: PMC2862747
3.  A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis 
PLoS ONE  2010;5(4):e10137.
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).
Methodology
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.
Significance
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’.
doi:10.1371/journal.pone.0010137
PMCID: PMC2855702  PMID: 20419126

Results 1-3 (3)