bioRxivSystems BiologyPreprint
Comparative gene regulatory network analysis in Alzheimer's disease and major depressive disorder identifies shared core regulatory circuits
Abstract
Alzheimers disease and major depressive disorder are prevalent, devastating conditions with limited treatment options. Recent insights suggest that despite distinct phenotypes, these disorders share similar processes such as neuroinflammation. However, the extent of overlapping biological processes and their underlying molecular mechanisms remain to be elucidated. Therefore, we adopted a computational systems biology approach to compare regulatory programs in the prefrontal cortex of both disorders. Leveraging publicly available RNA sequencing data on different human cohorts, both at bulk and single-cell level, and using diverse computational methodologies, we inferred gene regulatory networks, which model the molecular interactions between transcription factors and their target genes, and characterized cell-type-specific regulatory programs and biological pathways. We identified core regulatory circuits shared in both disorders, including transcription factors that play a pivotal role in microglial activation such as IKZF1, IRF8, RUNX1 and SPI1. Most of these transcription factors had a reported role in Alzheimers, but not in depression. We found several common pathways such as microglial activation, but also more disease-specific pathways. Through orthogonal data analysis, we were able to validate several of the predicted regulatory interactions in Alzheimers disease and major depressive disorder. In summary, our work revealed neuroinflammation and microglial activation in both diseases, under the control of shared core regulatory circuits. The potential relevance of these transcription factors and genes warrants additional investigation, especially in depression, offering possible novel therapeutic opportunities.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
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