Abstract
BackgroundRheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide. To date, there is no cure for the disease and current treatments show undesirable side effects. As the disease affects a growing number of individuals, and during their working age, the gathering of all information able to improve therapies -by understanding their and the disease mechanisms of action- represents an important area of research, benefiting not only patients but also societies. In this direction, network analysis methods have been used in previous work to further our understanding of this complex disease, leading to the identification of CRKL as a potential drug target for treatment of RA. Here, we use computational methods to expand on this work, testing the hypothesis in silico.ResultsAnalysis of the CRKL network -available at http://www.picb.ac.cn/ClinicalGenomicNTW/software.html- allows for investigation of the potential effect of perturbing genes of interest. Within the group of genes that are significantly affected by simulated perturbation of CRKL, we are lead to further investigate the importance of PXN. Our results allow us to (1) refine the hypothesis on CRKL as a novel drug target (2) indicate potential causes of side effects in on-going trials and (3) importantly, provide recommendations with impact on on-going clinical studies.ConclusionsBased on a virtual network that collects and connects a large number of the molecules known to be involved in a disease, one can simulate the effects of controlling molecules, allowing for the observation of how this affects the rest of the network. This is important to mimic the effect of a drug, but also to be aware of -and possibly control- its side effects. Using this approach in RA research we have been able to contribute to the field by suggesting molecules to be targeted in new therapies and more importantly, to warrant efficacy, to hypothesise novel recommendations on existing drugs currently under test.
Highlights
IntroductionRheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide
Following the public availability of the Rheumatoid arthritis (RA) network used to inform this study, the network analysed here is made publically available at [26], where is can be saved as a graphml file, suitable for running Signalling Petri Net (SPN) in BioLayout Express. For both scenarios, there was little difference in the manner in which expression levels of molecules changed over the twenty time points, with both scenarios resulting in two distinct groups of profiles: namely those that reach a high expression level in a short number of time points and a small number of less consistent more scattered profiles
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time point the expression profile patterns of the molecules that are significantly affected perturbation of CRKL, namely ABL1, PXN, RHOQ, RAPGEF1 and RAP1B
Summary
Rheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide. As the disease affects a growing number of individuals, and during their working age, the gathering of all information able to improve therapies -by understanding their and the disease mechanisms of action- represents an important area of research, benefiting patients and societies. In this direction, network analysis methods have been used in previous work to further our understanding of this complex disease, leading to the identification of CRKL as a potential drug target for treatment of RA. More advanced therapies that target focused pathways (anti-TNFα [10]) and reduce -but do not eliminate- this type of side effect are extremely costly, whereas traditional approaches receive controversial favour and lack molecular evidence [11]
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