Neuronal Resilience and Transcriptional Regulatory Programs
Four Fellows are examining how gene regulatory mechanisms shape neuronal vulnerability and resilience in Parkinson’s disease. Their projects investigate how disease-linked mutations, aging-associated regulatory processes, and noncoding genomic elements alter neuronal states through disrupted RNA splicing, enhancer activity, and epigenomic regulation, including transposable elements. By integrating single-cell and multi-omic profiling with computational modeling and targeted perturbations, these projects aim to define causal regulatory pathways that drive degeneration or promote resilience, generating predictive frameworks and biomarker-relevant insights to support precision therapeutic strategies.
Jonathan Brenton, PhD
Project Title: Dissecting splicing dysregulation caused by LRRK2 mutations through multi-omics profiling
Home Team: Hardy (Hardy Lab)
Host Team: Alessi (Alessi Lab)
Institution: University College London
Project Summary: Recent work has identified widespread splicing dysregulation in LRRK2 p.G2019S brain tissues and models. Building upon these novel findings, this project will generate an integrated map of splicing and proteomic changes in brain tissue from three disease-causing LRRK2 mutations to comprehensively dissect LRRK2 Parkinson’s disease mis-splicing. The most dysregulated targets identified will be screened in publicly available datasets for their biomarker potential. Candidate transcripts will also be inserted into neuronal cell lines to understand their impact on cellular function, providing new diagnostic and therapeutic strategies for Parkinson’s disease.
J. Kate Brynildsen, PhD
Project Title: Network control of neuronal resilience in Parkinson’s disease
Home Team: Biederer (Bassett Lab)
Host Team: Gradinaru (Gradinaru Lab)
Institution: University of Pennsylvania
Project Summary: This project investigates mechanisms of neuronal resilience in Parkinson’s disease by integrating computational modeling with experimental validation. Using a duplex network control theory framework, neurons will be modeled across two layers: cell type identity and activity-dependent transcription. Simulations will predict how stimulation of defined inputs influences resilience-associated gene expression. Predictions will be validated with optogenetics, single-cell transcriptomics, and AAV-based perturbations in Parkinson’s-relevant circuits. This research will develop a computational framework linking input-specific activity to gene expression and will experimentally test and refine predictions in substantia nigra neurons. This integrative approach seeks to identify resilience-conferring genes and pathways in Parkinson’s disease.
Raquel Garza Gomez, PhD
Project Title: Dynamic regulatory role of Transposable Elements through human aging and Parkinson’s disease
Home Team: Jakobsson (Jakobsson Lab)
Host Team: Voet (Voet Lab)
Institution: Lund University
Project Summary: Aging is the primary risk factor for Parkinson’s disease, featuring epigenetic alterations that correlate with chronological age. Notably, Parkinson’s disease patients exhibit accelerated epigenetic aging. Previous work has identified Transposable Elements (TEs) as dynamic brain regulatory sequences responsive to epigenetic changes and linked to Parkinson’s disease. This project will profile aging- and Parkinson’s disease-associated epigenetic changes in TEs at single-cell and spatial resolution. By integrating existing long-read DNA, short-read single-cell multi-omic data, novel long-read single-cell epigenome-plus-transcriptome sequencing, and TE-centric bioinformatics, this project has the potential to redefine aging and Parkinson’s disease paradigms while developing multi-omics tools and analytical frameworks.
Weiqiang Liu, PhD
Project Title: Causal mapping of enhancer-gene networks in Parkinson’s disease via single-cell perturb-seq and AI
Home Team: Scherzer (Dong Lab)
Host Team: Rio (Soldner Lab)
Institution: Yale University School of Medicine
Project Summary: Enhancers are critical regulators of gene expression, yet their functions remain poorly understood in Parkinson’s disease. Leveraging the Parkinson5D multi-omics dataset, this project will prioritize ~1,000 high-confidence Parkinson’s disease-relevant enhancers and silence them in iPSC-derived neurons and glia using CRISPRi, followed by single-cell RNA sequencing. These perturbational data will train an AI model to simulate enhancer activity and predict their downstream effects in Parkinson’s disease patient cohorts. This integrative approach will map causal enhancer-gene relationships, reconstruct Parkinson’s disease-specific regulatory networks, and generate openly available datasets and predictive tools, providing mechanistic insights that may guide therapeutic discovery in neurodegeneration.
