bioRxivNeurosciencePreprint
Progranulin deficiency induces lipid droplet accumulation in microglia via a STAT3-GPAT3 axis
Jun LiiD, Yiyue Shi, Haoyuan GuaniD, Yifei LüiD, Tuo Yi, Wei Li, Yini Wang, Yeting Guo, Bei LiiD, Caihong ZhuiD
Abstract
Heterozygous mutations in the progranulin (PGRN) encoding gene GRN cause frontotemporal dementia (FTD), whereas homozygous GRN mutations lead to neuronal ceroid lipofuscinosis (NCL). However, the mechanisms underlying neurodegeneration due to PGRN deficiency remain unclear. In the aged brains or under neurodegenerative conditions, the accumulation of lipid droplets (LDs) in microglia contributes to cellular dysfunction and pro-inflammatory responses, exacerbating neurodegenerative pathology. Here, we investigated how PGRN deficiency induces LD accumulation in microglia. We found that PGRN ablation significantly upregulated triglycerol-3-phosphate acyltransferase 3 (GPAT3), the first and rate-limiting enzyme for triacylglycerol biosynthesis, and increased LD formation in both Grn-/- BV2 microglial cells and Grn-/-mouse brains. Mechanistic study revealed that PGRN deficiency upregulates GPAT3 via stimulating signal transduction and activator of transcription factor 3 (STAT3) signaling. Silencing Gpat3, inhibiting GPAT3 activity with the small molecule inhibitor FSG67, or PGRN restoration reduced LD accumulation and rescued the cytotoxicity of Grn-/- microglia conditional medium (MCM) toward N2a cells. Notably, intraperitoneal administration of FSG67 or AAV-MG1.2-mCx3cr1-mGrn-eGFP-mediated microglia-specific gene therapy reduced microglial LDs and ameliorated behavior phenotypes in Grn-/- mice. Our study elucidates the role of PGRN loss in microglial lipid homeostasis and identifies the STAT3-GPAT3 axis as a potential therapeutic target for LD-associated neurodegenerative diseases.
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 1
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