title: Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects.
abstract: 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.
authors.affiliation: IAC - Istituto per le Applicazioni del Calcolo "Mauro Picone," CNR - Consiglio Nazionale delle Ricerche Rome, Italy ; Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China.
authors.forename: Paolo
authors.initials: P
authors.lastname: Tieri
authors.affiliation: Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China.
authors.forename: XiaoYuan
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China.
authors.forename: Lisha
authors.initials: L
authors.lastname: Zhu
authors.affiliation: Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China.
authors.forename: Christine
authors.initials: C
authors.lastname: Nardini
keywords: host-microbiome interface; multi-omic data integration; network topology; protein-protein interaction; rheumatoid arthritis