Biology subjects

Ryan, G.

Publications and source records attributed to Ryan, G..

4 recordsLinked to original sources

bioRxiv2026

ARID5B mutations cause a neurodevelopmental syndrome with neuroinflammation episodes

Genetic disorders affecting the epigenetic machinery constitute a major group of neurodevelopmental conditions. Pathogenic variants in several ARID transcription factors--particularly ARID1A, ARID1B, and ARID2--cause Coffin-Siris syndromes, all characterized by intellectual disability (ID). These genes encode core subunits of the BRG1/BRM-associated factor (BAF) chromatin remodeling complex. In contrast, ARID family members that function in other regulatory complexes have remained largely unexplored in neurodevelopmental disease. Here, we identify 29 individuals carrying heterozygous ARID5B variants, of which 24 (83%) introduce premature termination codons in the exceptionally long final exon, one affects the exon 9 splice donor site, and four are missense variants in conserved domains within the N-terminal half of the protein. Using a CRISPR-Cas9 knock-in mouse model harboring the p.Q522Ter variant, together with in vitro assays, we investigated the functional consequences of C-terminal ARID5B truncations. All affected individuals presented with global developmental delay or ID--most commonly mild--and frequent speech and language impairment. Recurrent features included kidney malformations, behavioral difficulties, and recurrent infections of the respiratory and urinary tracts. Two individuals experienced central nervous system inflammation, and two infants presented with persistent pulmonary hypertension. Remarkably, 19 of 29 variants (66%) cluster within the first quarter of exon 10, are de novo, and escape nonsense-mediated mRNA decay (NMD), which we confirmed for two variants affecting seven individuals. Variants outside this region were inherited. Heterozygous mice exhibited developmental and behavioral abnormalities, while homozygous mutations was perinatally lethal. Truncations and a small deletion within a predicted nuclear localization signal (NLS) caused cytosolic mislocalization of ARID5B, whereas the isolated C-terminal half retained nuclear localization, suggesting an independent distal NLS. Collectively, these findings define ARID5B-related neurodevelopmental disorder as a distinct clinical entity and reveal how disruption of specific ARID5B domains impacts protein localization, mammalian development, immune and neurobehavioral function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/698931v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@158e8d9org.highwire.dtl.DTLVardef@1f014dorg.highwire.dtl.DTLVardef@18e6e7forg.highwire.dtl.DTLVardef@1f886e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2024

Spike mRNA Vaccine Encapsulated in a Lipid Nanoparticle Composed of Phospholipid 1,2-Dioleoyl-sn-Glycero-3-Phosphoethanolamine Induced Potent B- and T-cell Responses Associated with Protection against COVID-19 in Hamsters

Lipid nanoparticles (LNPs) have recently emerged as one of the most advanced vehicle platforms for efficient in vivo delivery of nucleoside-modified mRNA vaccine, particularly for COVID-19. LNPs comprise four different lipids: ionizable lipids, helper or neutral lipids, cholesterol, and lipids attached to polyethylene glycol (PEG). Studies on using the mRNA-LNP platform for vaccines have largely focused on the nucleic acid cargo with less attention to the LNP vehicle. While the LNPs protect mRNA from degradation and efficiently deliver the mRNA to antigen-presenting cells the effect of lipid composition and biophysical properties on the immunogenic and protective mRNA vaccine remain to be fully elucidated. In the present study, we used SARS-CoV-2 Spike-mRNA as a prototype vaccine, to study the effect of 4 different of LNPs with various lipid compositions. We demonstrate that when the same Spike-mRNA was delivered in the LNP4 formulation based on phospholipid 1,2-dioleoyl-sn-glycero-3- Phosphoethanolamine it outperformed the immunogenicity and protective efficacy of three LNPs (LNP1, LNP2, and LNP3) that are based on different lipids. Compared to other three LNPs, the LNP4: (i) enhanced phenotypic and functional maturation of dendritic cells; (ii) induced strong T-cell responses, (iii) increased secretion of proinflammatory, pro-follicular T helper (Tfh) cell cytokines; (iv) induced higher neutralization IgG titers; and (v) and provided better protection against SARS-CoV-2 infection and COVID-19 in the hamster model. We discussed the potential mechanisms by which LNP which include the phospholipid 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine may activate protective B- and T-cell responses.

immunology↗

bioRxiv2023

A Scalable High Throughput Fully Automated Pipeline for the Quantification of Amyloid Pathology in Alzheimer's Disease Using Deep Learning Algorithms

The most common approach to characterize neuropathology in Alzheimers disease (AD) involves a manual survey and inspection by an expert neuropathologist of postmortem tissue that has been immunolabeled to visualize the presence of amyloid {beta} in plaques and around blood vessels and neurofibrillary tangles of the tau protein. In the case of amyloid {beta} pathology, a semiquantitative score is given that is based on areas of densest pathology. The approach has been well-validated but the process is laborious and time consuming, and inherently susceptible to intra- and inter-observer variability. Moreover, the tremendous growth in genetic, transcriptomic and proteomic data from AD patients has created new opportunities to link clinical features of AD to molecular pathogenesis through pathology, but the lack of high throughput quantitative and comprehensive approaches to assess neuropathology limits the associations that can be discovered. To address these limitations, we designed a computational pipeline to analyze postmortem tissue from AD patients in a fully automated, unbiased and high throughput manner. We used deep learning to train algorithms with a Mask Regional-Convolutional Neural Network to detect and classify different types of amyloid pathology with human level accuracy. After training on pathology slides from a Mt Sinai cohort, our algorithms identified amyloid pathology in samples made at an independent brain bank and from an unrelated cohort of patients, indicating that the algorithms were detecting reproducible and generalizable pathology features. We designed the pipeline to retain the position of the pathology it detects, making it possible to reconstruct a map of pathology across the entire whole slide image, facilitating neuropathological analyses at multiple scales. Quantitative measurements of amyloid pathology correlated positively and significantly with the severity of AD as measured by standard approaches. We conclude that we have developed a computational pipeline to analyze digitized images of neuropathology in high throughput and algorithms to detect types of amyloid pathology with human level accuracy that should enable neuropathological analysis of large tissue collections and integration of those results with orthogonal clinical and multiomic measurements.

neuroscience↗

bioRxiv2020

Super-human cell death detection with biomarker-optimized neural networks

Cell death is an essential process in biology that must be accounted for in live microscopy experiments. Nevertheless, cell death is difficult to detect without perturbing experiments with stains, dyes or biosensors that can bias experimental outcomes, lead to inconsistent results, and reduce the number of processes that can be simultaneously labelled. These additional steps also make live microscopy difficult to scale for high-throughput screening because of the cost, labor, and analysis they entail. We address this fundamental limitation of live microscopy with biomarker-optimized convolutional neural networks (BO-CNN): computer vision models trained with a ground truth biosensor that detect live cells with superhuman, 96% accuracy more than 100 times faster than previous methods. Our models learn to identify important morphological characteristics associated with cell vitality without human input or additional perturbations, and to generalize to other imaging modalities and cell types for which they have no specialized training. We demonstrate that we can interpret decisions from BO-CNN models to gain biological insight into the patterns they use to achieve superhuman accuracy. The BO-CNN approach is broadly useful for live microscopy, and affords a powerful new paradigm for advancing the state of high-throughput imaging in a variety of contexts.

cell biology↗