GSE126096
A familial Alzheimer's disease-like mutation in the zebrafish presenilin 1 gene affects brain energy production
To prevent or ameliorate Alzheimer's disease (AD) requires that we understand its molecular basis. AD develops over decades but detailed molecular analysis of AD brains is limited to postmortem tissue where the stresses initiating the disease may be obscured by compensatory responses and neurodegenerative processes. Rare, dominant mutations in a small number of genes drive early onset of familial AD (EOfAD). Numerous transgenic models of AD have been constructed in mouse and other organisms based on these mutant genes, but transcriptomic analysis of these models has raised serious doubts regarding their representation of the disease state. Objective: In the absence of an understanding of the molecular mechanism(s) underlying AD, we posit that the most valid approach is to model the human genetic state as closely as possible – i.e. to analyse single, heterozygous EOfAD-like mutations of endogenous genes in intact brains. We used genome editing to introduce an EOfAD-like mutation (Q96_K97del) into the endogenous presenilin 1 (psen1) gene of zebrafish. We analysed transcriptomes of young adult (6-month-old) entire brains from a family of heterozygous mutant and wild type sibling fish. Gene ontology (GO) analysis revealed effects on mitochondria, particularly ATP synthesis, and on ATP-dependent processes including vacuolar acidification. Overall design: We generated a family of heterozygous mutant (Q96K97/+ at psen1) and wild type (+/+) sibling zebrafish by mating a psen1Q96K97del/+ individual with a +/+ individual and raising the progeny from a single spawning event together in one tank. Since AD is more prevalent in human females than males, and to further reduce gene expression “noise” in our analyses, we obtained brain transcriptome data from four female wild type fish and four female heterozygous mutant fish. Note: While the results in the associated publication are based on these 6-month-old samples only, the differential gene expression analyses also used additional samples from the same batch (see GSE149149) to obtain more accurate variance estimates.