1
|
Xu Z, Wang J, Wang G. Weighted gene co-expression network analysis for hub genes in colorectal cancer. Pharmacol Rep 2024; 76:140-153. [PMID: 38150140 DOI: 10.1007/s43440-023-00561-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/16/2023] [Accepted: 11/20/2023] [Indexed: 12/28/2023]
Abstract
BACKGROUND This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA). METHODS Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established. RESULTS After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC. CONCLUSIONS CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.
Collapse
Affiliation(s)
- Zheng Xu
- Department of Oncology Surgery, Beidahuang Industry Group General Hospital, Harbin, 150088, Heilongjiang, People's Republic of China
| | - Jianing Wang
- Department of Gastrointestinal Surgery, Beidahuang Industry Group General Hospital, Harbin, 150088, Heilongjiang, People's Republic of China
| | - Guosheng Wang
- Department of Pancreaticobiliary Surgery, The First Affiliated Hospital of Harbin Medical University, No. 23, Post Street, Nangang District, Harbin, 150007, Heilongjiang, People's Republic of China.
| |
Collapse
|
2
|
Li H, Hu X, Li J, Jiang W, Wang L, Tan X. Identification of key regulatory genes and their working mechanisms in type 1 diabetes. BMC Med Genomics 2023; 16:8. [PMID: 36650594 PMCID: PMC9843847 DOI: 10.1186/s12920-023-01432-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/04/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D. METHODS The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms. RESULTS A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes. CONCLUSION An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D.
Collapse
Affiliation(s)
- Hui Li
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| | - Xiao Hu
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| | - Jieqiong Li
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| | - Wen Jiang
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| | - Li Wang
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| | - Xin Tan
- grid.508008.50000 0004 4910 8370Pediatric Department, The First Hospital of Changsha, No. 311, Yingpan Road, Kaifu District, Changsha, 410000 Hunan People’s Republic of China
| |
Collapse
|
3
|
Lu JM, Chen YC, Ao ZX, Shen J, Zeng CP, Lin X, Peng LP, Zhou R, Wang XF, Peng C, Xiao HM, Zhang K, Deng HW. System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes. Genes Immun 2018; 20:500-508. [PMID: 30245508 DOI: 10.1038/s41435-018-0045-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 07/17/2018] [Accepted: 07/19/2018] [Indexed: 12/28/2022]
Abstract
Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.
Collapse
Affiliation(s)
- Jun-Min Lu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Yuan-Cheng Chen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Zeng-Xin Ao
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Lin-Ping Peng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Rou Zhou
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Xia-Fang Wang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Cheng Peng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, PR China
| | - Hong-Mei Xiao
- School of Basic Medical Sciences, Central South University, Changsha, 410000, Hunan, PR China
| | - Kun Zhang
- Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, 70125, USA
| | - Hong-Wen Deng
- School of Basic Medical Sciences, Central South University, Changsha, 410000, Hunan, PR China. .,Southern Medical University, Guangzhou, 510515, Guangdong, PR China. .,Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA.
| |
Collapse
|
4
|
Zhu W, Shen H, Zhang JG, Zhang L, Zeng Y, Huang HL, Zhao YC, He H, Zhou Y, Wu KH, Tian Q, Zhao LJ, Deng FY, Deng HW. Cytosolic proteome profiling of monocytes for male osteoporosis. Osteoporos Int 2017; 28:1035-1046. [PMID: 27844135 PMCID: PMC5779619 DOI: 10.1007/s00198-016-3825-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 10/27/2016] [Indexed: 11/30/2022]
Abstract
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.
Collapse
Affiliation(s)
- W Zhu
- College of Life Sciences, Hunan Normal University, Changsha, Hunan, 410081, China
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - H Shen
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - J-G Zhang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - L Zhang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Y Zeng
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
- College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, 100044, China
| | - H-L Huang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Y-C Zhao
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - H He
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Y Zhou
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - K-H Wu
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Q Tian
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - L-J Zhao
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - F-Y Deng
- Center for Genetic Epidemiology and Genomics, Soochow University School of Public Health, Suzhou, Jiangsu, 215123, China
| | - H-W Deng
- College of Life Sciences, Hunan Normal University, Changsha, Hunan, 410081, China.
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.
- College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, 100044, China.
| |
Collapse
|
5
|
Xiao X, Hao J, Wen Y, Wang W, Guo X, Zhang F. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: An analysis. Bone Joint Res 2016; 5:314-9. [PMID: 27445359 PMCID: PMC5005471 DOI: 10.1302/2046-3758.57.2000502] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 06/07/2016] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES The molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA. METHODS We first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING. RESULTS For RA synovium, the top-ranked gene module is HLA-A, containing TAP2, HLA-A, HLA-C, TAPBP and LILRB1 genes. For RA PBMCs, the top-ranked gene module is GRB7, consisting of HLA-DRB5, HLA-DRA, GRB7, CD63 and KIT genes. Functional enrichment analysis identified three significant GO terms for RA synovium, including antigen processing and presentation of peptide antigen via major histocompatibility complex class I (false discovery rate (FDR) = 4.86 × 10 - 4), antigen processing and presentation of peptide antigen (FDR = 2.33 × 10 - 3) and eukaryotic translation initiation factor 4F complex (FDR = 2.52 × 10 - 2). CONCLUSION This study reported several RA-associated gene modules and their functional association networks.Cite this article: X. Xiao, J. Hao, Y. Wen, W. Wang, X. Guo, F. Zhang. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: an analysis. Bone Joint Res 2016;5:314-319. DOI: 10.1302/2046-3758.57.2000502.
Collapse
Affiliation(s)
- X Xiao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| | - J Hao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| | - W Wang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| | - X Guo
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Yanta West Road 76, Xi'an, Shaanxi, China
| |
Collapse
|
6
|
Chen YC, Guo YF, He H, Lin X, Wang XF, Zhou R, Li WT, Pan DY, Shen J, Deng HW. Integrative Analysis of Genomics and Transcriptome Data to Identify Potential Functional Genes of BMDs in Females. J Bone Miner Res 2016; 31:1041-9. [PMID: 26748680 DOI: 10.1002/jbmr.2781] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2015] [Revised: 12/27/2015] [Accepted: 12/30/2015] [Indexed: 02/01/2023]
Abstract
Osteoporosis is known to be highly heritable. However, to date, the findings from more than 20 genome-wide association studies (GWASs) have explained less than 6% of genetic risks. Studies suggest that the missing heritability data may be because of joint effects among genes. To identify novel heritability for osteoporosis, we performed a system-level study on bone mineral density (BMD) by weighted gene coexpression network analysis (WGCNA), using the largest GWAS data set for BMD in the field, Genetic Factors for Osteoporosis Consortium (GEFOS-2), and a transcriptomic gene expression data set generated from transiliac bone biopsies in women. A weighted gene coexpression network was generated for 1574 genes with GWAS nominal evidence of association (p ≤ 0.05) based on dissimilarity measurement on the expression data. Twelve distinct gene modules were identified, and four modules showed nominally significant associations with BMD (p ≤ 0.05), but only one module, the yellow module, demonstrated a good correlation between module membership (MM) and gene significance (GS), suggesting that the yellow module serves an important biological role in bone regulation. Interestingly, through characterization of module content and topology, the yellow module was found to be significantly enriched with contractile fiber part (GO:044449), which is widely recognized as having a close relationship between muscle and bone. Furthermore, detailed submodule analyses of important candidate genes (HOMER1, SPTBN1) by all edges within the yellow module implied significant enrichment of functional connections between bone and cytoskeletal protein binding. Our study yielded novel information from system genetics analyses of GWAS data jointly with transcriptomic data. The findings highlighted a module and several genes in the model as playing important roles in the regulation of bone mass in females, which may yield novel insights into the genetic basis of osteoporosis. © 2016 American Society for Bone and Mineral Research.
Collapse
Affiliation(s)
- Yuan-Cheng Chen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Yan-Fang Guo
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China.,Institute of Bioinformatics, School of Basic Medical Science, Southern Medical University, Guangzhou, PR China
| | - Hao He
- Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.,Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, USA
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Xia-Fang Wang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Rou Zhou
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Wen-Ting Li
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Dao-Yan Pan
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China
| | - Hong-Wen Deng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, PR China.,Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA
| |
Collapse
|
7
|
Abstract
Osteoporosis is characterized by low bone mass and an increased risk of fracture. Genetic factors, environmental factors and gene-environment interactions all contribute to a person's lifetime risk of developing an osteoporotic fracture. This Review summarizes key advances in understanding of the genetics of bone traits and their role in osteoporosis. Candidate-gene approaches dominated this field 20 years ago, but clinical and preclinical genetic studies published in the past 5 years generally utilize more-sophisticated and better-powered genome-wide association studies (GWAS). High-throughput DNA sequencing, large genomic databases and improved methods of data analysis have greatly accelerated the gene-discovery process. Linkage analyses of single-gene traits that segregate in families with extreme phenotypes have led to the elucidation of critical pathways controlling bone mass. For example, components of the Wnt-β-catenin signalling pathway have been validated (in both GWAS and functional studies) as contributing to various bone phenotypes. These notable advances in gene discovery suggest that the next decade will witness cataloguing of the hundreds of genes that influence bone mass and osteoporosis, which in turn will provide a roadmap for the development of new drugs that target diseases of low bone mass, including osteoporosis.
Collapse
|
8
|
He H, Lin D, Zhang J, Wang Y, Deng HW. Biostatistics, Data Mining and Computational Modeling. TRANSLATIONAL BIOINFORMATICS 2016. [DOI: 10.1007/978-94-017-7543-4_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
9
|
Zhou Y, Deng HW, Shen H. Circulating monocytes: an appropriate model for bone-related study. Osteoporos Int 2015; 26:2561-72. [PMID: 26194495 DOI: 10.1007/s00198-015-3250-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Accepted: 07/10/2015] [Indexed: 10/23/2022]
Abstract
Peripheral blood monocytes (PBMs) are an important source of precursors of osteoclasts, the bone-resorbing cells and the cytokines produced by PBMs that have profound effects on osteoclast differentiation, activation, and apoptosis. So PBMs represent a highly valuable and unique working cell model for bone-related study. Finding an appropriate working cell model for clinical and (epi-)genomic studies of human skeletal disorders is a challenge. Peripheral blood monocytes (PBMs) can give rise to osteoclasts, the bone-resorbing cells. Particularly, PBMs provide the sole source of osteoclast precursors for adult peripheral skeleton where the bone marrow is normally hematopoietically inactive. PBMs can secrete potent pro- and anti-inflammatory cytokines, which are important for osteoclast differentiation, activation, and apoptosis. Reduced production of PBM cytokines represents a major mechanism for the inhibitory effects of sex hormones on osteoclastogenesis and bone resorption. Abnormalities in PBMs have been linked to various skeletal disorders/traits, strongly supporting for the biological relevance of PBMs with bone metabolism and disorders. Here, we briefly review the origin and further differentiation of PBMs. In particular, we discuss the close relationship between PBMs and osteoclasts, and highlight the utility of PBMs in study the pathophysiological mechanisms underlying various skeletal disorders.
Collapse
Affiliation(s)
- Y Zhou
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA
- Cell and Molecular Biology Department, Tulane University, New Orleans, LA, 70118, USA
| | - H-W Deng
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA
- Cell and Molecular Biology Department, Tulane University, New Orleans, LA, 70118, USA
| | - H Shen
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA.
- Cell and Molecular Biology Department, Tulane University, New Orleans, LA, 70118, USA.
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
| |
Collapse
|