Back

Long term rescue of Alzheimer deficits in vivo by one-time gene-editing of App C-terminus.

Aulston, B.; Gimse, K.; Bazick, H. O.; Kramar, E. A.; Pizzo, D. P.; Parra-Rivas, L. A.; Sun, J.; Branes-Guerrero, K.; Checka, N.; Bagheri, N.; Satyadev, N.; Carlson-Stevermer, J.; Saito, T.; Saido, T. C.; Audhya, A.; Wood, M. A.; Zylka, M. J.; Saha, K.; Roy, S.

2025-01-07 neuroscience
10.1101/2024.06.08.598099 bioRxiv
Show abstract

Gene-editing technologies promise to create a new class of therapeutics that can achieve permanent correction with a single intervention. Besides eliminating mutant alleles in familial disease, gene-editing can also be used to favorably manipulate upstream pathophysiologic events and alter disease-course in wider patient populations, but few such feasible therapeutic avenues have been reported. Here we use CRISPR-Cas9 to edit the last exon of amyloid precursor protein (App), relevant for Alzheimers disease (AD). Our strategy effectively eliminates an endocytic (YENPTY) motif at APP C-terminus, while preserving the N-terminus and compensatory APP-homologues. This manipulation favorably alters events along the amyloid-pathway - inhibiting toxic APP-{beta}-cleavage fragments (including A{beta}) and upregulating neuroprotective APP--cleavage products. AAV-driven editing ameliorates neuropathologic, electrophysiologic, and behavioral deficits in an AD knockin mouse model. Effects persist for many months, and no abnormalities are seen in WT mice even after germline App-editing; underlining overall efficacy and safety. Pathologic alterations in the glial-transcriptome of App-KI mice, as seen by single nuclei RNA-sequencing (sNuc-Seq), are also normalized by App C-terminus editing. Our strategy takes advantage of innate transcriptional rules that render terminal exons insensitive to nonsense-decay, and the upstream manipulation is expected to be effective for all forms of AD. These studies offer a path for a one-time disease-modifying treatment for AD.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.