Gentzel, R. | Search bioRxiv

bioRxiv2026

Restoring Parkin Function: An AAV Gene Therapy Approach for Early-Onset Parkinson's Disease

BackgroundBiallelic loss-of-function mutations in PRKN gene (encoding Parkin protein) cause early-onset Parkinsons disease (EOPD). Parkin is a crucial component of PINK1-Parkin pathway, which marks damaged mitochondria for degradation via mitophagy. Without functional Parkin, damaged mitochondria accumulate, causing oxidative stress and neurodegeneration. ObjectiveInvestigate Parkin gene replacement via AAV gene therapy as a potential treatment for Parkin-dependent EOPD. MethodsWe initially validated phosphorylated ubiquitin Ser65 (pUbSer65) as an indicator of Parkin-mediated mitophagy initiation. We evaluated AAV-mediated PRKN replacement (hereafter, AAV-Parkin) in a Parkin knockout neuroblastoma cell line (SH-SY5Y cells) and feasibility of delivery in mouse and rat models. ResultsOur research showed pUbSer65 signal was reduced in Parkin-KO SH-SY5Y cells when compared to wild-type cells after mitochondrial stress, indicating deficiency in initiation of mitophagy. AAV-mediated human PRKN gene replacement successfully restored these pUbSer65 levels in knockout cells. We saw restoration in patient-derived fibroblasts following AAV-Parkin overexpression. We developed a translatable gene therapy approach using rodents. We demonstrated the feasibility of delivering AAV-Parkin directly into the substantia nigra (SN) of wild-type rats. Using an AAV1 capsid with Ef1a promoter, we achieved dose-dependent Parkin expression and identified a well-tolerated dose. We also evaluated multiple promoters in a proprietary Spark100 capsid, finding Ef1a and Synapsin1 (Syn1) were most effective for transducing dopaminergic neurons in the SN of mice without causing adverse effects. These findings established a well-tolerated vector dose and an optimal capsid-promoter combination. ConclusionsOur results support the potential of AAV-Parkin gene therapy as a disease-modifying approach for Parkin-deficient EOPD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/737487v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@16dd13corg.highwire.dtl.DTLVardef@c3dfcdorg.highwire.dtl.DTLVardef@19a310dorg.highwire.dtl.DTLVardef@a66f2_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

AnNoBrainer, an Automated Annotation of Mouse Brain Images using Deep Learning

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that less rigorous, less time-consuming methods of histopathological assessment are employed by non-pathologists, especially for early discovery and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ([~]50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts histopathological assessment standards (>85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

neuroscience↗