A combined proteome and microarray investigation of inorganic phosphate-induced pre-osteoblast cells

Inorganic phosphate, which is generated during osteoblast differentiation and mineralization, has recently been identified as an important signaling molecule capable of altering signal transduction pathways and gene expression. A large scale quantitative proteomic investigation of pre-osteoblasts stimulated with inorganic phosphate for 24 h resulted in the identification of 2501 proteins, of which 410 (16%) had an altered abundance ratio of greater than or equal to 1.75-fold, either up or down, revealing both novel and previously defined osteoblast-regulated proteins. A pathway/function analysis of these proteins revealed an increase in cell cycle and proliferation that was subsequently verified by conventional biochemical means. To further analyze the mechanisms by which inorganic phosphate regulates cellular protein levels, we undertook a mRNA microarray analysis of preosteoblast cells at 18, 21, and 24 h after inorganic phosphate exposure. Comparison of the mRNA microarray data with the 24-hour quantitative proteomic data resulted in a generally weak overall correlation; the 21-hour RNA sample showed the highest correlation to the proteomic data. However, an analysis of osteoblast relevant proteins revealed a much higher correlation at all time points. A comparison of the microarray and proteomic datasets allowed for the identification of a number of candidate proteins that are post-transcriptionally regulated by elevated inorganic phosphate, including Fra-1, a member of the activator protein-1 family of transcription factors. The analysis of the data presented here not only sheds new light on the important roles of inorganic phosphate in osteoblast function but also begins to address the contribution of post-transcriptional and post-translational regulation to a cell's expressed proteome. The ability to accurately measure changes in both protein abundance and mRNA levels on a system-wide scale represents a novel means to extract data from previously one-dimensional datasets.