Identification of Proteins Important for Male Osteoporosis Osteoporosis is a major public health problem, mainly characterized by low bone mineral density (BMD). Variation of BMD is largely genetically determined (heritability of >60%). Some BMD genes/genomic regions are gender specific. Although women have lower BMD than men, men suffer significantly higher mortality rate upon osteoporotic fractures. However, studies on osteoporosis have largely been focused on women. Few genetic epidemiological studies and no proteomic epidemiological study on osteoporosis have been performed on men. Bone marrow mesenchymal stem cells (BMMSCs) and peripheral blood monocytes (PBMs) are precursors for osteoblasts (bone formation cells) and osteoclasts (bone resorption cells), respectively. Proteomics is a powerful state-of-the-art strategy in genetic dissection of complex diseases, such as osteoporosis. However, a major problem affecting the power of current most proteomic studies is the limited detection of low abundance proteins and proteins with extreme isoelectric point, molecular weight, and hydrophobicity, especially membrane proteins. A NOVEL approach to this problem is subcellular proteome extraction to stepwise isolate proteins from membrane, cytosol, nucleus, and cytoskeleton E fractions followed by sensitive 2D-nanoLC-ESI-MS/MS for fractioning and identifying significant proteins. Our hypothesis is that changes in the protein expression profiles in BMMSCs and PBMs underlie molecular mechanisms of BMD variation and are associated with osteoporosis in men. Our major goals here are to identify proteins differentially expressed in BMMSCs and PBMs in men with high vs. low BMD and thus identify proteins (and their genes) associated with male osteoporosis in BMMSCs and PBMs. We will recruit 120 otherwise healthy Caucasian males at peak bone mass ages of 25-50, including 60 subjects with low and 60 with high BMD (age matched population bottom or top 20% respectively in terms of BMD). Half of the sample (30 low vs. 30 high BMD subjects) will serve as the """"""""discovery cohort"""""""" and the other half (30 low vs. 30 high BMD subjects) will serve as the """"""""replication cohort"""""""". We will take fresh bone marrow and peripheral blood samples from each male subject, as we do in our ongoing NIH SCOR projects for female subjects. BMMSCs and PBMs will be isolated by subcellular proteome extraction for membrane proteins together with proteins of cytosolic, nuclear, and cytoskeletal fractions. Proteomic profiling experiments and analyses will be performed on the isolated protein samples from the discovery E cohort using 2D-nanoLC-ESI-MS/MS . Significant differentially expressed proteins identified will be verified by Western blotting using samples from the replication cohort. The major results (particularly those obtained from PBMs) of this study may be used to design customary diagnostic protein antibody chips and/or protein markers for prognosis of male osteoporosis. In combination with our ongoing projects for identifying risk genes of osteoporosis through genome-wide DNA association scan and genome-wide mRNA expression study of osteogenic cells, this study will powerfully and efficiently identify genes and some of their functions for male osteoporosis.

Public Health Relevance

Osteoporosis is a disease with severe bone loss and a significantly increased risk of low trauma fractures. Male osteoporosis, compared with female osteoporosis, has higher mortality rate upon fractures. This project aims to identify proteins that are important to male osteoporosis. The findings will provide essential scientific basis for effective prevention, diagnosis and treatment of the disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
5R01AR057049-04
Application #
8143422
Study Section
Special Emphasis Panel (ZRG1-HOP-T (02))
Program Officer
Sharrock, William J
Project Start
2009-09-18
Project End
2014-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
4
Fiscal Year
2011
Total Cost
$619,978
Indirect Cost
Name
Tulane University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118
Zhao, Yan; Ning, Yujie; Zhang, Feng et al. (2018) PCA-based GRS analysis enhances the effectiveness for genetic correlation detection. Brief Bioinform :
Liu, Hui-Min; He, Jing-Yang; Zhang, Qiang et al. (2018) Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 293:225-235
Liang, Xiao; Wu, CuiYan; Zhao, Hongmou et al. (2018) Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis. Bone 116:301-306
Liu, Li; Wen, Yan; Zhang, Lei et al. (2018) Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study. J Clin Endocrinol Metab 103:1850-1855
Li, Yumei; Xiang, Yang; Xu, Chao et al. (2018) Rare variant association analysis in case-parents studies by allowing for missing parental genotypes. BMC Genet 19:7
Xu, Chao; Fang, Jian; Shen, Hui et al. (2018) EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits. Bioinformatics 34:1996-2003
Lin, Xu; Peng, Cheng; Greenbaum, Jonathan et al. (2018) Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches. Mol Genet Genomics 293:711-723
Zhu, W; Shen, H; Zhang, J-G et al. (2017) Cytosolic proteome profiling of monocytes for male osteoporosis. Osteoporos Int 28:1035-1046
Xu, Chao; Zhang, Ji-Gang; Lin, Dongdong et al. (2017) A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease. G3 (Bethesda) 7:2271-2279
He, Hao; Lin, Dongdong; Zhang, Jigang et al. (2017) Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network. BMC Bioinformatics 18:149

Showing the most recent 10 out of 159 publications