bioRxivPathology
Transcriptomic signature and pro-osteoclastic secreted factors of abnormal bone marrow stromal cells in fibrous dysplasia
Z. Michel, L. N. Raborn, T. Spencer, K. S. Pan, D. Martin, K. L. Roszko, Y. Wang, P. Robey, A. Boyce, M. T. Collins, L. F. de Castro
Abstract
Fibrous dysplasia (FD) is a mosaic skeletal disorder caused by somatic activating variants in GNAS, encoding for Gs, which leads to excessive cAMP signaling in bone marrow stromal cells (BMSCs). Despite advancements in our understanding of FD pathophysiology, the effect of Gs activation in the BMSC transcriptome remains unclear, as well as how this translates into their local influence in the lesional microenvironment. In this study, we analyzed changes induced by Gs activation in BMSC transcriptome and performed a comprehensive analysis of their production of cytokines and other secreted factors. We performed RNAseq of cultured BMSCs from patients with FD and healthy volunteers, and from an inducible mouse model of FD, and combined their transcriptomic profiles to build a robust FD BMSC genetic signature. Pathways related to Gs activation, cytokine signaling, and extracellular matrix deposition were identified. In addition, a comprehensive profile of their secreted cytokines and other factors was performed to identify modulation of several key factors we hypothesized to be involved in FD pathogenesis. We also screened circulating cytokines in a collection of plasma samples from patients with FD, finding positive correlations of several cytokines to their disease burden score, as well as to one another and bone turnover markers. Overall, these data support a pro-inflammatory, pro-osteoclastic behavior of BMSCs bearing hyperactive Gs variants, and point to several cytokines and other secreted factors as possible therapeutic targets and/or circulating biomarkers for FD.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: cell type annotation
- research focus: cell-cell communication
- approach: statistical model
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