Tenofovir treatment of primary osteoblasts alters gene expression profiles: implications for bone mineral density loss

Abstract

There is strong clinical evidence that implicates tenofovir in the loss of bone mineral density during treatment of human immunodeficiency virus infection. In this study, we sought to test the hypothesis that tenofovir treatment of osteoblasts causes changes in the gene expression profile that would impact osteoblast function during bone formation. Primary osteoblasts were isolated and then treated with the tenofovir prodrug, tenofovir disoproxil fumarate (TDF). Total RNA from TDF-treated and untreated osteoblasts were extracted and used for microarray analysis to assess TDF-associated changes in the gene expression profile. Strikingly, the changes in gene expression profiles involved in cell signaling, cell cycle and amino acid metabolism, which would likely impact osteoblast function in bone formation. Our findings demonstrate for the first time that tenofovir treatment of primary osteoblasts results in gene expression changes that implicate loss of osteoblast function in tenofovir-associated bone mineral density loss.

Fig. 1

Tenofovir structure and primary osteoblast cell viability following drug exposure. The structure of tenofovir (A) and tenofovir disoproxil fumarate (TDF, the prodrug of tenofovir) (B) is shown. C. Viability of primary osteoblasts following exposure to TDF. A TDF dilution series was added to primary osteoblast cultures for 3 days, refreshing every 24h. Cell viability was determined by ATP detection as described in the Materials and Methods.

Fig. 2

Heat map of gene expression profile from TDF-treated and untreated primary osteoblasts as determined from microarray analysis. Two gene chips were used in each replicate experiment and a total of four independent replicate experiments were performed. Hierarchical clustering analysis of gene expression data from primary osteoblasts exposed to TDF was also performed. The dendrograms were generated based on average linkage hierarchical clustering of expression data from 79 probe sets. Significant change was defined as having an absolute fold change >= 1.5 and a t-test p-value of <= 0.05. Signal values were log2 transformed, row mean centered and then subjected to unsupervised hierarchical clustering by the UPGMA algorithm using Pearson Correlation as the similarity metric.