Literature DB >> 26435169

Network-based proteomic analysis for postmenopausal osteoporosis in Caucasian females.

Lan Zhang1, Yao-Zhong Liu1, Yong Zeng1,2, Wei Zhu1, Ying-Chun Zhao1, Ji-Gang Zhang1, Jia-Qiang Zhu1, Hao He1, Hui Shen1, Qing Tian1, Fei-Yan Deng1,3, Christopher J Papasian4, Hong-Wen Deng1,2.   

Abstract

Menopause is one of the crucial physiological events during the life of a woman. Transition of menopause status is accompanied by increased risks of various health problems such as osteoporosis. Peripheral blood monocytes can differentiate into osteoclasts and produce cytokines important for osteoclast activity. With quantitative proteomics LC-nano-ESI-MS(E) (where MS(E) is elevated-energy MS), we performed protein expression profiling of peripheral blood monocytes in 42 postmenopausal women with discordant bone mineral density (BMD) levels. Traditional comparative analysis showed proteins encoded by four genes (LOC654188, PPIA, TAGLN2, YWHAB) and three genes (LMNB1, ANXA2P2, ANXA2) were significantly down- and upregulated, respectively, in extremely low- versus high-BMD subjects. To study functionally orchestrating groups of detected proteins in the form of networks, we performed weighted gene coexpression network analysis and gene set enrichment analysis. Weighted gene coexpression network analysis showed that the module including the annexin gene family was most significantly correlated with low BMD, and the lipid-binding related GO terms were enriched in this identified module. Gene set enrichment analysis revealed that two significantly enriched gene sets may be involved in postmenopausal BMD variation by regulating pro-inflammatory cytokines activities. To gain more insights into the proteomics data generated, we performed integrative analyses of the datasets available to us at the genome (DNA level), transcriptome (RNA level), and proteome levels jointly.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Bioinformatics; GSEA; LC-MS; Menopause; Peripheral blood monocyte; WGCNA

Mesh:

Substances:

Year:  2015        PMID: 26435169     DOI: 10.1002/pmic.201500005

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  22 in total

Review 1.  P4 medicine and osteoporosis: a systematic review.

Authors:  Klemen Kodrič; Klemen Čamernik; Darko Černe; Radko Komadina; Janja Marc
Journal:  Wien Klin Wochenschr       Date:  2016-11-21       Impact factor: 1.704

2.  Differentially expressed proteins identified by TMT proteomics analysis in bone marrow microenvironment of osteoporotic patients.

Authors:  Q Zhou; F Xie; B Zhou; J Wang; B Wu; L Li; Y Kang; R Dai; Y Jiang
Journal:  Osteoporos Int       Date:  2019-02-09       Impact factor: 4.507

3.  Quantitative proteomics and integrative network analysis identified novel genes and pathways related to osteoporosis.

Authors:  Yong Zeng; Lan Zhang; Wei Zhu; Chao Xu; Hao He; Yu Zhou; Yao-Zhong Liu; Qing Tian; Ji-Gang Zhang; Fei-Yan Deng; Hong-Gang Hu; Li-Shu Zhang; Hong-Wen Deng
Journal:  J Proteomics       Date:  2016-05-03       Impact factor: 4.044

4.  Network based subcellular proteomics in monocyte membrane revealed novel candidate genes involved in osteoporosis.

Authors:  Y Zeng; L Zhang; W Zhu; H He; H Sheng; Q Tian; F-Y Deng; L-S Zhang; H-G Hu; H-W Deng
Journal:  Osteoporos Int       Date:  2017-07-24       Impact factor: 4.507

5.  Proteomic studies of bone and skeletal health outcomes.

Authors:  Carrie M Nielson; Jon M Jacobs; Eric S Orwoll
Journal:  Bone       Date:  2019-04-04       Impact factor: 4.398

6.  Integrative analysis of transcriptome-wide association study data and mRNA expression profiles identified candidate genes and pathways associated with atrial fibrillation.

Authors:  Lu Zhang; Li Liu; Mei Ma; Shiqiang Cheng; Bolun Cheng; Ping Li; Yan Wen; Yanan Du; Xiao Liang; Yan Zhao; Miao Ding; Qi Xin; Chujun Liang; Huimei Huang; Feng Zhang
Journal:  Heart Vessels       Date:  2019-05-07       Impact factor: 2.037

7.  Long-term changes in plasma proteomic profiles in premenopausal and postmenopausal Black and White women: the Atherosclerosis Risk in Communities study.

Authors:  Duke Appiah; Pamela J Schreiner; James S Pankow; Guy Brock; Weihong Tang; Faye L Norby; Erin D Michos; Christie M Ballantyne; Aaron R Folsom
Journal:  Menopause       Date:  2022-08-20       Impact factor: 3.310

8.  Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease.

Authors:  Qi Zhang; Cheng Ma; Marla Gearing; Peng George Wang; Lih-Shen Chin; Lian Li
Journal:  Acta Neuropathol Commun       Date:  2018-03-01       Impact factor: 7.801

Review 9.  A road map for understanding molecular and genetic determinants of osteoporosis.

Authors:  Tie-Lin Yang; Hui Shen; Anqi Liu; Shan-Shan Dong; Lei Zhang; Fei-Yan Deng; Qi Zhao; Hong-Wen Deng
Journal:  Nat Rev Endocrinol       Date:  2019-12-02       Impact factor: 43.330

Review 10.  "Omics" Signatures in Peripheral Monocytes from Women with Low BMD Condition.

Authors:  Bhavna Daswani; M Ikram Khatkhatay
Journal:  J Osteoporos       Date:  2018-03-18
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