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TNF Conference 2009: Beyond Bones – RANKL/RANK in the Immune System

  • Andreas Leibbrandt
  • Josef M. Penninger
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 691)

Abstract

Bone-related diseases, such as osteoporosis or rheumatoid arthritis, affect hundreds of millions of people worldwide and pose a tremendous burden to health care. Over the last decade, a major focus of research was the identification of factors controlling bone metabolism and osteoclastogenesis, respectively. In particular, three tumor necrosis factor (TNF) family molecules, the receptor activator of NF-κB (RANK), its ligand RANKL, and the decoy receptor of RANKL, osteoprotegerin (OPG), have been shown to be central regulators of osteoclast development and osteoclast function. Moreover, in a series of experiments it was conclusively shown that RANKL-expressing T cells also drive osteoclastogenesis. These findings also provided a rationale for the bone loss observed in patients with a chronically activated immune system, such as in rheumatoid arthritis or leukemias, arguing that activated T cells and other immune cells via RANKL induce osteoclastogenesis and thereby can shift the intricate balance of bone deposition and resorption. Through knockout mice it became also clear that the RANKL–RANK–OPG system is involved in other processes, such as in controlling immune responses in the skin, lymph node organogenesis, development of AIRE+ thymic medullary epithelial cells, and formation of a lactating mammary gland during pregnancy. Importantly, RANKL–RANK have become rationale drug targets for therapy of bone loss associated with multiple diseases that literally affect hundreds of millions of people. Here we primarily discuss the role of RANKL and RANK in the immune system.

Keywords

Bone Loss Rheumatoid Arthritis Patient Rank Signaling RANKL Expression Bone Marrow Cavity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.IMBA, Institute of Molecular Biotechnology of the Austrian Academy of SciencesViennaAustria

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