Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects.

Paolo Tieri,Lisha Zhu,Christine Nardini,Xiaoyuan Zhou

doi:10.3389/fcell.2014.00059

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.

Highlights

Rheumatoid arthritis (RA) is a multifaceted autoimmune, chronic and inflammatory disease with, to date, unclear etiology
The latter controls immune alterations and blocks innate response to pathogens. 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 Growth factor receptor-bound protein 2 (GRB2) as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens
While protocols and medical best practice recommendations become mature in this direction, we propose the use of network approaches and omics from diverse origins including genomics, epigenomics, transcriptomics, post-transcriptomics, proteomics, and host-microbiome interface to gut intestinal (GI) metagenomics, to appropriately monitor the complexity of the disease

Summary

Introduction

Rheumatoid arthritis (RA) is a multifaceted autoimmune, chronic and inflammatory disease with, to date, unclear etiology. While protocols and medical best practice recommendations become mature in this direction, we propose the use of network approaches and omics from diverse 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 complexity of the disease. The novelty of the present work, lies in its application to RA, and in the number of omic layers we have used, from genomic to proteomic and including the host-microbiome interface.

Methods

Results

Conclusion