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Bone & Joint Research
Vol. 6, Issue 12 | Pages 649 - 655
1 Dec 2017
Liu Y Zhu H Hong H Wang W Liu F

Objectives

Recently, high failure rates of metal-on-metal (MOM) hip implants have raised concerns of cobalt toxicity. Adverse reactions occur to cobalt nanoparticles (CoNPs) and cobalt ions (Co2+) during wear of MOM hip implants, but the toxic mechanism is not clear.

Methods

To evaluate the protective effect of zinc ions (Zn2+), Balb/3T3 mouse fibroblast cells were pretreated with 50 μM Zn2+ for four hours. The cells were then exposed to different concentrations of CoNPs and Co2+ for four hours, 24 hours and 48 hours. The cell viabilities, reactive oxygen species (ROS) levels, and inflammatory cytokines were measured.


Bone & Joint Research
Vol. 6, Issue 10 | Pages 572 - 576
1 Oct 2017
Wang W Huang S Hou W Liu Y Fan Q He A Wen Y Hao J Guo X Zhang F

Objectives

Several genome-wide association studies (GWAS) of bone mineral density (BMD) have successfully identified multiple susceptibility genes, yet isolated susceptibility genes are often difficult to interpret biologically. The aim of this study was to unravel the genetic background of BMD at pathway level, by integrating BMD GWAS data with genome-wide expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (meQTLs) data

Method

We employed the GWAS datasets of BMD from the Genetic Factors for Osteoporosis Consortium (GEFOS), analysing patients’ BMD. The areas studied included 32 735 femoral necks, 28 498 lumbar spines, and 8143 forearms. Genome-wide eQTLs (containing 923 021 eQTLs) and meQTLs (containing 683 152 unique methylation sites with local meQTLs) data sets were collected from recently published studies. Gene scores were first calculated by summary data-based Mendelian randomisation (SMR) software and meQTL-aligned GWAS results. Gene set enrichment analysis (GSEA) was then applied to identify BMD-associated gene sets with a predefined significance level of 0.05.


Bone & Joint Research
Vol. 5, Issue 7 | Pages 314 - 319
1 Jul 2016
Xiao X Hao J Wen Y Wang W Guo X Zhang F

Objectives

The molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA.

Methods

We first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING.