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Senescent preosteoclast secretome promotes metabolic syndrome-associated osteoarthritis through COX2-PGE2

Cite this dataset

Su, Weiping (2022). Senescent preosteoclast secretome promotes metabolic syndrome-associated osteoarthritis through COX2-PGE2 [Dataset]. Dryad.


Metabolic syndrome–associated osteoarthritis (MetS-OA) is a distinct osteoarthritis phenotype defined by the coexistence of MetS or its individual components. Despite the high prevalence of MetS-OA, its pathogenic mechanisms are unclear. Here, we report that humans and mice with MetS are more likely to develop osteoarthritis-related subchondral bone alterations than those without MetS. MetS-OA mice exhibited a rapid increase in joint subchondral bone plate and trabecular thickness before articular cartilage degeneration. Subchondral preosteoclasts undergo senescence at the pre- or early-osteoarthritis stage and acquire a unique secretome to stimulate osteoblast differentiation and inhibit osteoclast differentiation. Antagonizing preosteoclast senescence markedly mitigates pathological subchondral alterations and osteoarthritis progression in MetS-OA mice. At the molecular level, preosteoclast secretome activates COX2-PGE2, resulting in stimulated differentiation of osteoblast progenitors for subchondral bone formation. Administration of a selective COX2 inhibitor attenuated subchondral bone alteration and osteoarthritis progression in MetS-OA mice. Longitudinal analyses of the human Osteoarthritis Initiative (OAI) cohort dataset also revealed that COX2 inhibitor use, relative to non-selective nonsteroidal anti-inflammatory drug use, is associated with less progression of osteoarthritis and subchondral bone marrow lesion worsening in participants with MetS-OA. Our findings suggest a central role of a senescent preosteoclast secretome-COX2/PGE2 axis in the pathogenesis of MetS-OA.


Preosteoclasts were challenged with H2O2 (200 µM for 2 hours, then 20 µmol for 1 day) or vehicle (control). The group has 3 samples. We completed the analysis of the Agilgent gene expression profiling chip of the samples. RNA quantity and quality were assessed using NanoDrop ND-1000. RNA integrity was assessed by standard denaturing gel electrophoresis. Sequences were collected from a wide range of sources, then validated and optimized by alignment to the assembled mouse genome. Sample labeling and chip hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology) with minor modifications. Total RNA from each sample was linearly amplified and labeled with Cy3-UTP. Labeled cRNAs were purified using the RNeasy Mini Kit (Qiagen) and assayed for concentration and activity with a NanoDrop ND-1000. Chip hybridization. The hybridization chip was washed, mounted and scanned (Agilent DNA Microarray Scanner (part number G2505C)). Use the Agilent Feature Extraction software (v11.0.1.1) to obtain the chip map, and read the value to obtain the raw data. Raw data were subjected to Quantile normalization and subsequent data processing using GeneSpring GX v12.1 software (Agilent Technologies). After standardization of the raw data, high-quality probes (a probe based on the proportion of qualified markers Detected) are screened for further analysis. 


National Institute on Aging, Award: R01AG068226