Quantitative proteomics and integrative network analysis identified novel genes and pathways related to osteoporosis☆
Graphical abstract
Introduction
Osteoporosis is a global public health problem with a high heritability, and mainly characterized by low BMD [1]. Osteoporosis has become one of the most serious public health problems around the world, leading to millions of fractures annually [1], [2]. Among fractures at various skeletal sites, hip fracture is the most important owing to its high prevalence, high morbidity and mortality, and excessive therapeutic cost [3], [4].
Monocytes are bone marrow-derived circulating leukocytes that can further differentiate into various cell types like macrophages and dendritic cells [5]. The morphology of mature monocytes in the peripheral circulation is heterogeneous, and these cells constitute 5–10% of peripheral blood leukocytes in humans [6]. Human peripheral blood monocytes (PBMs) may serve as precursors of osteoclasts and produce cytokines important for osteoclast differentiation, activation, and apoptosis as well as acting as one of the most important target cells for sex hormones in bone metabolism [7], [8]. In the bone field, PBMs have already been well-established/accepted as a well working excellent cell model for studying gene/protein expression patterns and their regulation mechanisms in relation to osteoporosis risk in vivo in humans [9], [10], [11], [12], [13], [14]. Numerous studies highlighted the feasibility and utility of employing in vivo PBMs to study novel pathophysiological mechanisms during the process of osteoclastogenesis underlying osteoporosis risk [8], [12]. Therefore, PBMs are one major class of cells that are functionally relevant to the pathogenesis of osteoporosis and they have been successfully used for etiology studies in the bone field.
Network analysis can identify the correlation and topology between different proteins. Pathways/modules generated by network analysis may reflect the biological processes more comprehensively and objectively than single protein/gene analysis [15]. Based on the utilization of functional information and topological information, there are quite some approaches to perform gene enrichment analysis, pathway analysis, functional cluster analysis and network reconstruction analysis [16]. For instance, DAVID (Database for Annotation, Visualization and Integrated Discovery) is a popular knowledgebase including multiple online tools which can provide abundant functional information from multiple databases for a list of genes. However, the topological features generated from DAVID may be quite limited [16], [17]. In contrast, as a frequently-used platform for network analyses, the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database engine can provide more topological information [16], [18]. Furthermore, Cytoscape, an open source software project for data integration, network analysis and visualization [19], is a powerful tool with hundreds of comprehensive and timely-updated applications which can provide multiple functions of network analyses.
With sensitive LC-nano-ESI-MSE (Liquid Chromatograph-nano-Electrospray Ionization-Mass Spectrometry) based quantitative proteomic analysis, we identified a number of proteins that contribute to the pathogenesis of osteoporosis individually. Different kinds of tools were then used for network analysis to maximize the coverage of significant biological information related to bone metabolism. DAVID was used for the preliminary functional annotation and gene enrichment analysis. STRING and Cytoscape were applied for comprehensive network reconstruction and visualization. Additionally, in order to add more evidence to the results from our quantitative proteomic study, an in silico replication study was performed at genome-wide level.
Section snippets
Human subjects
Our study was approved by Institutional Review Boards of University of Missouri Kansas City and Tulane University. All the subjects signed consent forms before being enrolled into this study. All subjects were self-identified as European Caucasian females. 33 subjects were recruited in this study. The lumbar spine BMD (g/cm2) and hip BMD (which is the combined value of three regions including femoral neck, trochanter and interchochanter) were measured by using Hologic 4500 W dual energy X-ray
Proteomic profiling
In this study, a total of 2058 proteins were identified in 33 samples by LC-nano-ESI-MSE. To reduce false proteins detection, 1142 out of 2058 proteins, which were detected in 5 or more subjects were used for further analyses [12] (Supplemental Table 1). Furthermore, a global normalization method was applied for data quality control in order to minimize the variability due to sample preparation or equipment conditions. We applied log2 transformation of our data for further analyses.
Identification of differentially-expressed proteins (DEPs)
We applied
Discussion
With its potential to differentiate into osteoclasts and the production of potent pro-inflammatory and anti-inflammatory cytokines, PBMs are likely to play broad and critical roles in bone metabolism [30], [31]. Abnormal behavior in PBM formation, migration, differentiation, functional activity, and apoptosis may be closely related to the development of human skeletal disorders [24]. In particular, it has now been well established by numerous studies that multi-omics and molecular studies using
Conclusions
In our study, label-free based quantitative proteomics integrated with network analysis strongly supported the contribution of the genes ITGA2B, GSN and RHOA and the pathways “regulation of actin cytoskeleton” and “leukocyte transendothelial migration” to osteoporosis risk. The results were further verified in multiple level studies including protein-RNA integrative analysis and genome wide association study.
The following are the supplementary data related to this article.
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Acknowledgement
This study was partially supported by grants from NIH (P50AR055081, R21AG27110, R01AR057049, R01AR050496, and R01AG026564), Franklin D. Dickson/Missouri Endowment and Edward G. Schlieder Endowment. The study also benefited from the National Natural Science Foundation of China (31371194) and the Fundamental Research Funds from the Central Universities (2013JBM098).
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All authors state that they have no conflicts of interest.