bioRxivNeurosciencePreprint
Unexpected opposing biological effect of genetic risk factors for Parkinson disease
Marcus KeatingeiD, Matthew E. GeggiD, Lisa Watson, Heather MortiboysiD, Nan LiiD, Mark DunningiD, Deepak Ailani, Hai Hoang Bui, Astrid van Rens, Dirk J. LefeberiD, Ryan B. MacDonaldiD, Anthony H.V. SchapiraiD, Oliver BandmanniD
Abstract
The additive effect of genetic risk variants on overall disease risk is a plausible but frequently unproven hypothesis. To test this hypothesis, we assessed the biological effect of combined glucocerebrosidase (GCase) and acid sphingomyelinase (ASM) deficiency. Variants in both glucocerebrosidase1 (GBA1) and sphingomyelinase (SMPD1) are genetic risk factors for Parkinsons disease. Unexpectedly, ASM deficiency resulted in normalized behaviour and prolonged survival in gba1-/-;smpd1-/- double-mutant zebrafish compared to gba1-/-. RNAseq-based pathway analysis confirmed a profound rescue of neuronal function and intracellular homeostasis. We identified complete reciprocal rescue of mitochondrial respiratory chain function and abolished lipid membrane oxidation in gba1-/-;smpd1-/- compared to gba1-/- or smpd1-/- as the underlying rescue mechanism. Complementing in vitro experiments demonstrated an unexpected reduction of -synuclein levels in human cell lines with combined GCase and ASM deficiency. Our study highlights the importance of functional validation for any putative interactions between genetic risk factors and their overall effect on disease-relevant mechanisms rather than readily assuming an additive effect. SummaryThe additive effect of genetic risk variants on disease risk is a popular but typically unproven hypothesis. We investigated this hypothesis mechanistically for Parkinsons disease risk factors and provide evidence of an unexpected rescue effect on neuronal function and survival.
- organism: zebrafish
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 1
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