bioRxivDevelopmental BiologyPreprint
Transcriptomic Landscape and Immune Microenvironment AroundWound Bed Define Regenerative versus Non-regenerative Outcomesin Mouse Digit Amputation
Archana PrabahariD, Connie S. ChamberlainiD, Ray Vanderby, William L. MurphyiD, William D’AngeloiD, Kulkarni Mangesh, Bryan N. BrowniD, Barsanjit MazumderiD, Stephen F. BadylakiD, Peng JiangiD
Abstract
In the mouse distal terminal phalanx (P3), it remains unclear why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (BMP2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, with potential applications in personalized treatment strategies and assessing the impact of interventions on regenerative outcomes.
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: benchmarking
- approach: deep learning
- Citations
- 1
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