Geller, A. | bioRxiv Science

bioRxiv2025

GPR34 regulation of disease-associated microglial states and responses to physiological stimuli

Expression of the G protein coupled receptor GPR34 is highly enriched in microglia and has been reported to be downregulated in several brain disease contexts, including Alzheimers disease (AD) and multiple sclerosis (MS). GPR34 function is poorly understood, as is its role in regulation of microglial states. Using RNA-sequencing, we find that microglia from Gpr34 knockout (KO) mouse brains exhibited a transcriptomic shift toward disease-associated microglia (DAM) and inflammatory profiles, partially resembling the microglial phenotype seen in 5xFAD AD model mice. Moreover, when Gpr34 KO mice were crossed with 5xFAD mice, the DAM transcriptional profile of microglia and glial pathology were further enhanced beyond the already robust DAM signature driven by 5xFAD alone. This occurred without affecting amyloid plaque burden. Human stem cell-derived microglia (iMGLs) lacking GPR34 showed reduced calcium (Ca{superscript 2}) and phosphorylated ERK (pERK) signaling in response to stimulation with known GPR34 agonists (lyso-phosphatidylserine (lysoPS) and myelin), as well as transcriptomic changes in immune regulation and cell proliferation related pathways. Interestingly, GPR34 KO iMGLs were selectively impaired in phagocytosis of myelin but not amyloid-{beta} (A{beta}) or E. coli, and showed a diminished transcriptional response elicited by myelin. Together, these findings suggest that GPR34 is important for maintaining microglia in a homeostatic state, promotes phagocytosis of and transcriptional response to myelin, and limits microglial activation in neurodegenerative disease conditions.

neuroscience↗

bioRxiv2025

EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene STXBP1

Missense variants in the STXBP1 gene are a frequent cause of early-onset developmental and epileptic encephalopathies and related neurodevelopmental disorders, but the clinical interpretation of these variants remains a major challenge. Most reported STXBP1 missense variants are classified as variants of uncertain significance (VUS), complicating diagnosis, counseling, and patient eligibility for precision therapies. Here, we developed EpiPred, a gene-specific machine learning classifier that predicts the pathogenicity of STXBP1 missense variants by integrating computational features with empirical evidence from cellular assays. Trained on a curated set of pathogenic and benign variants, EpiPred outperformed leading global prediction tools in accuracy, sensitivity, and specificity. We validated the models predictions using functional assays that measure protein abundance, solubility, stability, and interaction with the SNARE complex partner syntaxin 1. These biochemical readouts aligned closely with model outputs and enabled reclassification of several likely misdiagnosed variants. We deployed EpiPred in an interactive web application that allows clinicians, researchers, and patients to explore predictions for all possible missense variants in STXBP1. Our approach illustrates the power of gene-specific predictive modeling combined with experimental validation to improve variant interpretation and diagnostic resolution. By identifying likely pathogenic STXBP1 variants, including those that may respond to emerging therapies such as molecular chaperones, EpiPred supports more precise genetic diagnoses and offers a generalizable framework for other clinically relevant genes in neurological disease. ONE SENTENCE SUMMARYEpiPred improves STXBP1 variant interpretation, enabling precision genetic diagnoses and promoting access to targeted precision therapies

genetics↗