Wright, S. N. | bioRxiv Science

bioRxiv2025

Redefining Parkinson's Disease by Dysregulated Genetic Networks in Distinct Cell Types

Parkinsons disease (PD) is classically linked to dopaminergic neuron loss, but emerging evidence suggests broader cellular involvement. Here we show that PD risk variants converge on distinct molecular networks across specific brain cell types, enabling stratification of patients into six subgroups: dopaminergic, oligodendrocyte progenitor cells (O), excitatory (E), dopaminergic/excitatory, dopaminergic/oligodendrocyte and other. While all subgroups exhibit motor symptoms, the E-group individuals also display more severe non-motor symptoms, including dementia, hyposmia, and REM sleep behavior disorder. The O-group individuals exhibit reduced myelin integrity, as demonstrated by diffusion tensor imaging, implicating NRG6 (formerly C1orf56), a previously uncharacterized high-risk PD gene. We show that NRG6 encodes a conserved epidermal growth factor-like domain structurally and functionally analogous to neuregulin-1, which is critical for oligodendrocyte development and myelination. These findings redefine the cellular architecture of PD vulnerability and identify neuregulin-like signaling in oligodendrocytes as a potential contributor to non-motor symptoms.

neuroscience↗

bioRxiv2024

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

bioinformatics↗