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J Biol Chem. 2003 May 30;278(22):19723-31. Epub 2003 Mar 18.

Gene expression profiles and transcription factors involved in parathyroid hormone signaling in osteoblasts revealed by microarray and bioinformatics.

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Department of Physiology and Biophysics, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway 08854, USA.


Parathyroid hormone (PTH) binds to its receptor PTH1R (parathyroid hormone 1 receptor) in osteoblastic cells to regulate bone remodeling and calcium homeostasis. While prolonged exposure to PTH causes increased bone resorption, intermittent injections of PTH have an anabolic effect on bone. The molecular mechanisms regulating these processes are still largely unknown. Here, we present our results on gene expression profile changes in the PTH-treated osteoblastic cell line, UMR 106-01, using DNA microarray analysis. A total of 125 known genes and 30 unknown expressed sequence tags (ESTs) were found to have at least 2-fold expression changes after PTH treatment at 4, 12, and 24 h. 14 genes were previously known to be PTH-regulated but many were unknown to be regulated by PTH prior to our experiments. Real-time reverse transcriptase-PCR confirmed that 90 and 50% of the genes are regulated more than 2-fold by PTH in UMR 106-01 and rat primary osteoblastic cells, respectively. Most genes belong to the following protein families: hormones, growth factors, and receptors; signal transduction pathway proteins; transcription factors; proteases; metabolic enzymes; structural and matrix proteins; transporters; etc. These results provide a comprehensive and deeper knowledge about PTH regulation of osteoblastic gene expression. Next, we designed a computational method to extract information about transcription factors likely involved in regulating these genes. These factors include those previously known to be involved in PTH signaling (AP-1 and the cAMP response element-binding protein), those that were identified by microarray data (C/EBP), and some novel transcription factors (AP-2, AP-4, SP1, FoxD3, etc.). Our results suggest that a reliable bioinformatics approach can be easily applied for other systems.

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