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Bone & Joint Research
Vol. 13, Issue 9 | Pages 462 - 473
6 Sep 2024
Murayama M Chow SK Lee ML Young B Ergul YS Shinohara I Susuki Y Toya M Gao Q Goodman SB

Bone regeneration and repair are crucial to ambulation and quality of life. Factors such as poor general health, serious medical comorbidities, chronic inflammation, and ageing can lead to delayed healing and nonunion of fractures, and persistent bone defects. Bioengineering strategies to heal bone often involve grafting of autologous bone marrow aspirate concentrate (BMAC) or mesenchymal stem cells (MSCs) with biocompatible scaffolds. While BMAC shows promise, variability in its efficacy exists due to discrepancies in MSC concentration and robustness, and immune cell composition. Understanding the mechanisms by which macrophages and lymphocytes – the main cellular components in BMAC – interact with MSCs could suggest novel strategies to enhance bone healing. Macrophages are polarized into pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes, and influence cell metabolism and tissue regeneration via the secretion of cytokines and other factors. T cells, especially helper T1 (Th1) and Th17, promote inflammation and osteoclastogenesis, whereas Th2 and regulatory T (Treg) cells have anti-inflammatory pro-reconstructive effects, thereby supporting osteogenesis. Crosstalk among macrophages, T cells, and MSCs affects the bone microenvironment and regulates the local immune response. Manipulating the proportion and interactions of these cells presents an opportunity to alter the local regenerative capacity of bone, which potentially could enhance clinical outcomes. Cite this article: Bone Joint Res 2024;13(9):462–473


Bone & Joint Research
Vol. 11, Issue 8 | Pages 548 - 560
17 Aug 2022
Yuan W Yang M Zhu Y

Aims

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.

Methods

Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.


Bone & Joint Research
Vol. 7, Issue 12 | Pages 620 - 628
1 Dec 2018
Tätting L Sandberg O Bernhardsson M Ernerudh J Aspenberg† P

Objectives

Cortical and cancellous bone healing processes appear to be histologically different. They also respond differently to anti-inflammatory agents. We investigated whether the leucocyte composition on days 3 and 5 after cortical and cancellous injuries to bone was different, and compared changes over time using day 3 as the baseline.

Methods

Ten-week-old male C56/Bl6J mice were randomized to either cancellous injury in the proximal tibia or cortical injury in the femoral diaphysis. Regenerating tissues were analyzed with flow cytometry at days 3 and 5, using panels with 15 antibodies for common macrophage and lymphocyte markers. The cellular response from day 3 to 5 was compared in order to identify differences in how cancellous and cortical bone healing develop.