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Osteoporosis International

, Volume 28, Issue 3, pp 1035–1046 | Cite as

Cytosolic proteome profiling of monocytes for male osteoporosis

  • W. Zhu
  • H. Shen
  • J-G. Zhang
  • L. Zhang
  • Y. Zeng
  • H-L. Huang
  • Y-C. Zhao
  • H. He
  • Y. Zhou
  • K-H. Wu
  • Q. Tian
  • L-J. Zhao
  • F-Y. Deng
  • H-W. Deng
Original Article

Abstract

Summary

In male Caucasians with discordant hip bone mineral density (BMD), we applied the subcellular separation and proteome profiling to investigate the monocytic cytosol. Three BMD-associated proteins (ALDOA, MYH14, and Rap1B) were identified based on multiple omics evidence, and they may influence the pathogenic mechanisms of osteoporosis by regulating the activities of monocytes.

Introduction

Osteoporosis is a serious public health problem, leading to significant mortality not only in aging females but also in males. Peripheral blood monocytes (PBMs) play important roles in bone metabolism by acting as precursors of osteoclasts and producing cytokines important for osteoclast development. The first cytosolic sub-proteome profiling analysis was performed in male PBMs to identify differentially expressed proteins (DEPs) that are associated with BMDs and risk of osteoporosis.

Methods

Here, we conducted a comparative proteomics analysis in PBMs from Caucasian male subjects with discordant hip BMD (29 low BMD vs. 30 high BMD). To decrease the proteome complexity and expand the coverage range of the cellular proteome, we separated the PBM proteome into several subcellular compartments and focused on the cytosolic fractions, which are involved in a wide range of fundamental biochemical processes.

Results

Of the total of 3796 detected cytosolic proteins, we identified 16 significant (P < 0.05) and an additional 22 suggestive (P < 0.1) DEPs between samples with low vs. high hip BMDs. Some of the genes for DEPs, including ALDOA, MYH14, and Rap1B, showed an association with BMD in multiple omics studies (proteomic, transcriptomic, and genomic). Further bioinformatics analysis revealed the enrichment of DEPs in functional terms for monocyte proliferation, differentiation, and migration.

Conclusions

The combination strategy of subcellular separation and proteome profiling allows an in-depth and refined investigation into the composition and functions of cytosolic proteome, which may shed light on the monocyte-mediated pathogenic mechanisms of osteoporosis.

Keywords

Cytosolic proteome Glucose metabolism Osteoporosis Peripheral blood monocytes Regulation of the actin cytoskeleton 

Abbreviations

PBMs

Peripheral blood monocytes

BMD

Bone mineral density

DEPs

Differentially expressed proteins

ALDOA

Aldolase, fructose-bisphosphate A

MYH14

Myosin, heavy chain 14

RAP1B

Member of RAS oncogene family

GSN

Gelsolin

IL-1

Interleukin-1

IL-6

Interleukin-6

TNF-α

Tumor necrosis factor-α

MS

Mass spectrometer

PBMCs

Peripheral blood mononuclear cells

ATP

Adenosine triphosphate

RANKL

Receptor activator of nuclear factor kappa-B ligand

UPLC

Ultra performance liquid chromatography

HDMS

High-definition mass spectrometry

FA

Formic acid

ADH1

Alcohol dehydrogenase 1

GWAS

Genome-wide association studies

GO

Gene ontology

GEFOS

Genetic Factors for Osteoporosis Consortium

FDR

False discovery rate

DAVID

Database for Annotation, Visualization and Integrated Discovery

GO

Gene ontology

PPI

Protein-protein interactions

STRING

Search Tool for the Retrieval of Interacting Genes/Proteins

CFL1

Cofilin 1

FGG

Fibrinogen gamma chain

CBX3

Chromobox 3

CALR

Calreticulin

ACTN1

Actinin alpha 1

MYL9

Myosin light chain 9

ACTB

Actin, beta

HSP90AA1

Heat shock protein 90-kDa alpha family class A member 1

HSPA5

Heat shock protein family A (Hsp70) member 5

CAP1

Adenylate cyclase-associated protein 1

THBS1

Thrombospondin 1

TCA

Tricarboxylic acid

LDHAL6A

Lactate dehydrogenase A like 6A

LDHB

Lactate dehydrogenase B

TLN1

Talin1

M-CSF

Macrophage colony-stimulating factor

NMHC IIC

Non-muscle myosin II heavy chain

NM II

Muscle myosin II

Notes

Acknowledgements

This study was partially supported by and/or benefited from grants from National Institutes of Health (P50AR055081, R21AG27110, R01AR057049, R01AR059781) and Edward G. Schlieder Endowment to Tulane University.

This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study (FHS) and does not necessarily reflect the opinions of the FHS, Boston University. The GWA meta-analysis study dataset which used for supportive study in this manuscript was integrated from some individual GWAS datasets. The FHS datasets were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000007.v14.p6. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, of Genetic Determinants of Bone Fragility study was provided by the NIA Division of Geriatrics and Clinical Gerontology and the NIA Division of Aging Biology. Assistance with phenotype harmonization, SNP selection, data cleaning, meta-analyses, data management, and dissemination and general study coordination, was provided by the PAGE Coordinating Center (U01HG004801-01).

Compliance with ethical standards

Conflicts of interest

None.

Funding source

The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute NHLBI in collaboration with Boston University (Contract No. N01-HC-25195). Funding for SHARe Affymetrix genotyping was provided by NHLBI Contract N02-HL-64278. SHARe Illumina genotyping was provided under an agreement between Illumina and Boston University. Funding support for the Framingham BMD datasets was provided by NIH grants R01AR/AG 41398. Funding support for the Genetic Determinants of Bone Fragility (the Indiana fragility study) was provided through the NIA Division of Geriatrics and Clinical Gerontology. Genetic Determinants of Bone Fragility is a genome-wide association studies funded as part of the NIA Division of Geriatrics and Clinical Gerontology. Support for the collection of datasets and samples were provided by the parent grant, Genetic Determinants of Bone Fragility (P01-AG018397). Funding support for the genotyping which was performed at the Johns Hopkins University Center for Inherited Diseases Research was provided by the NIH NIA. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118, 32119, 32122, 42107-26, 42129-32, and 44221. WHI PAGE is funded through the NHGRI Population Architecture Using Genomics and Epidemiology (PAGE) network (Grant No. U01HG004790).

Supplementary material

198_2016_3825_MOESM1_ESM.docx (15 kb)
Supplementary Table 1 (DOCX 14 kb).
198_2016_3825_MOESM2_ESM.docx (22 kb)
Supplementary Table 2 (DOCX 22 kb).
198_2016_3825_MOESM3_ESM.docx (18 kb)
Supplementary Table 3 (DOCX 17 kb).

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Copyright information

© International Osteoporosis Foundation and National Osteoporosis Foundation 2016

Authors and Affiliations

  • W. Zhu
    • 1
    • 2
  • H. Shen
    • 2
  • J-G. Zhang
    • 2
  • L. Zhang
    • 2
  • Y. Zeng
    • 2
    • 3
  • H-L. Huang
    • 2
  • Y-C. Zhao
    • 2
  • H. He
    • 2
  • Y. Zhou
    • 2
  • K-H. Wu
    • 2
  • Q. Tian
    • 2
  • L-J. Zhao
    • 2
  • F-Y. Deng
    • 4
  • H-W. Deng
    • 1
    • 2
    • 3
  1. 1.College of Life SciencesHunan Normal UniversityChangshaChina
  2. 2.Center for Bioinformatics and Genomics, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  3. 3.College of Life Sciences and BioengineeringBeijing Jiaotong UniversityBeijingChina
  4. 4.Center for Genetic Epidemiology and GenomicsSoochow University School of Public HealthSuzhouChina

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