The map presented here assembles genomic, epigenomic, transcriptomic, post-transcriptomic, proteomic, and host-microbiome interface data related to RA, as detailed below, and integrates such info in the functional level of protein-protein relationships (PPIs). (GRB2) and Interleukin-1 Trifolirhizin Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former settings the activation of inflammatory, proliferative and degenerative pathways in sponsor and pathogens. The latter settings immune alterations and blocks innate response to pathogens. Conclusions: This multi-omic map properly recollects in one analytical picture known, yet complex, Trifolirhizin info like the adverse/side effects of MTX, and provides a reliable platform for hypothesis screening or recommendation on novel therapies. These results can support the development of RA translational study in COL4A1 the design of validation experiments and medical tests, as such we determine GRB2 like a strong 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 advertised, as this involves potential adverse effects in the form of impaired innate response to pathogens. data integration, host-microbiome interface, protein-protein connection, network topology Introduction Rheumatoid arthritis (RA) is definitely a multifaceted autoimmune, chronic and inflammatory disease with, to day, unclear etiology. As a consequence of its difficulty, the definition of efficient and effective treatments remains a remarkable challenge due to the troubles in controlling side effects and adverse events in relation to known (like genetic susceptibility, Stahl et al., 2010) and emergent (epigenomic factors, Nakano et al., 2012, dysbiosis, Scher and Abramson, 2011) RA-associated con-causes. Recently, translational study offers welcomed into medicine a number of novel perspectives. Among these, sequencing systems (screens) and computational rigorous methods (systems biology) right now coagulate into a practice where technology and mathematical modeling serve basic research in the production of selected hypotheses, which screening and ultimately in clinical studies can support medical study and practice (Okada et al., 2014; You et al., 2014). The recent acknowledgment of the importance and difficulty of the gut intestinal (GI) microbiome in the onset, progression and regression of RA (Scher and Abramson, 2011; Scher et al., 2012, 2013) and additional autoimmune diseases, requires to incorporate the effects within the GI microbiome for any novel therapy. While protocols and medical best practice recommendations become mature with this direction, we propose the use of network methods and from varied origins (i.e., different biochemical districts/compartments/layers) including genomics, epigenomics, transcriptomics, post-transcriptomics, proteomics, and host-microbiome interface to GI metagenomics, to appropriately monitor the difficulty of the disease. The novelty of the present work, therefore, lies not only in its software to RA, but also in the number of layers we have used, from genomic to proteomic and including the host-microbiome interface. These novelties allow to draw a single analytical picture of the fragmented molecular info available to day on RA, an very easily consultable and extendable research map for the experts in the field, andimportantlya systemic evaluation within the impact of a recently proposed RA therapeutic target (IRAK4), valuable and as an exemplar software of this approach. Overall, this work contributes to the general debate about data integration by offering details on our methodology, and to the area of complex inflammatory diseases, by providing specific examples of data choice and operational results. Methods Map construction The datasets used to construct the map are gathered from 13 different sources from databases and literature (Table ?(Table1).1). We included molecules experimentally associated to RA from manual curation of literature sources (dataset, dataset, set constitutes a more specific RA map, its extension offers a more systemic and practically usable map, notably in terms of the significance of the statistics that can be run on the extended map. The map presented here assembles genomic, epigenomic, transcriptomic, post-transcriptomic, proteomic, and host-microbiome interface data related to RA, as detailed below, and integrates such information at the functional level of protein-protein interactions (PPIs). The PPI framework is an assessed integrative approach (Hodgman, 2007; Dittrich et al., 2008; Jin et al., 2008; Kim et al., 2010; Iskar et al., Trifolirhizin 2012) that has already been used in computational biology to understand diseases’ pathogenesis (Huang et al., 2009b); to implement tools for the interpretation of inferred gene and protein lists (Berger et al., 2007; Antonov et al., 2009); to prioritize cancer-associated genes (Wu et al.,.