Showing papers by "Jian Luo published in 2016"

LGR4 is a receptor for RANKL and negatively regulates osteoclast differentiation and bone resorption.

Abstract: Tumor necrosis factor (TNF) superfamily member 11 (TNFSF11, also known as RANKL) regulates multiple physiological or pathological functions, including osteoclast differentiation and osteoporosis. TNFRSF11A (also called RANK) is considered to be the sole receptor for RANKL. Herein we report that leucine-rich repeat-containing G-protein-coupled receptor 4 (LGR4, also called GPR48) is another receptor for RANKL. LGR4 competes with RANK to bind RANKL and suppresses canonical RANK signaling during osteoclast differentiation. RANKL binding to LGR4 activates the Gαq and GSK3-β signaling pathway, an action that suppresses the expression and activity of nuclear factor of activated T cells, cytoplasmic, calcineurin-dependent 1 (NFATC1) during osteoclastogenesis. Both whole-body (Lgr4(-/-)) and monocyte conditional knockout mice of Lgr4 (Lgr4 CKO) exhibit osteoclast hyperactivation (including elevation of osteoclast number, surface area, and size) and increased bone erosion. The soluble LGR4 extracellular domain (ECD) binds RANKL and inhibits osteoclast differentiation in vivo. Moreover, LGR4-ECD therapeutically abrogated RANKL-induced bone loss in three mouse models of osteoporosis. Therefore, LGR4 acts as a second RANKL receptor that negatively regulates osteoclast differentiation and bone resorption.

Bortezomib Inhibits Giant Cell Tumor of Bone through Induction of Cell Apoptosis and Inhibition of Osteoclast Recruitment, Giant Cell Formation, and Bone Resorption

Abstract: Giant cell tumor of bone (GCTB) is a rare and highly osteolytic bone tumor that usually leads to an extensive bone lesion. The purpose of this study was to discover novel therapeutic targets and identify potential agents for treating GCTB. After screening the serum cytokine profiles in 52 GCTB patients and 10 normal individuals using the ELISA assay, we found that NF-κB signaling-related cytokines, including TNFα, MCP-1, IL1α, and IL17A, were significantly increased in GCTB patients. The results were confirmed by IHC that the expression and activity of p65 were significantly increased in GCTB patients. Moreover, all of the NF-κB inhibitors tested suppressed GCTB cell growth, and bortezomib (Velcade), a well-known proteasome inhibitor, was the most potent inhibitor in blocking GCTB cells growth. Our results showed that bortezomib not only induced GCTB neoplastic stromal cell (NSC) apoptosis, but also suppressed GCTB NSC-induced giant cell differentiation, formation, and resorption. Moreover, bortezomib specifically suppressed GCTB NSC-induced preosteoclast recruitment. Furthermore, bortezomib ameliorated GCTB cell-induced bone destruction in vivo As a result, bortezomib suppressed NF-κB-regulated gene expression in GCTB NSC apoptosis, monocyte migration, angiogenesis, and osteoclastogenesis. Particularly, the inhibitory effects of bortezomib were much better than zoledronic acid, a drug currently used in treating GCTB, in our in vitro experimental paradigms. Together, our results demonstrated that NF-κB signaling pathway is highly activated in GCTB, and bortezomib could suppress GCTB and osteolysis in vivo and in vitro, indicating that bortezomib is a potential agent in the treatment of GCTB. Mol Cancer Ther; 15(5); 854-65. ©2016 AACR.

Using the Ranking-Based KNN Approach for Drug Repositioning Based on Multiple Information

Abstract: Using effective computer methods to infer potential drug-disease relationships can provide clues for the discovery new uses of old drugs. This paper introduced a Ranking-based k-Nearest Neighbor (Re-KNN) method to drug repositioning for cardiovascular diseases. The main characteristic of the Re-KNN lies in combining conventional KNN algorithm with Ranking SVM (Support Vector Machine) algorithm to get neighbors that are more trustable. By integrating the chemical structural similarity, target-based similarity, side-effect similarity and topological similarity information, Re-KNN method can obtain an improved AUC (Area under ROC Curve) and AUPR (Area under Precision-Recall curve) compared with other methods, which prove the validity and efficiency of multiple features integration.