Biology subjects

Fogel, L.

Publications and source records attributed to Fogel, L..

4 recordsLinked to original sources

bioRxiv2026

Atherosclerosis destabilizes regulatory T cells (Tregs) resulting in multiple families of exTregs

How regulatory T cells (Tregs) lose lineage identity during chronic inflammation remains poorly understood. Here, using inducible Foxp3 lineage tracing together with single-cell transcriptomic, proteomic and T cell receptor (TCR) profiling in atherosclerosis-prone mice, we identify Treg destabilization as a staged and branching differentiation process rather than an abrupt loss of lineage identity. Conventional Tregs (cTregs) first transition through an effector Treg (eTreg) intermediate characterized by attenuation of the CD25-STAT5 axis while retaining core Treg features, before diversifying into eight transcriptionally distinct exTreg states, including Tfh-like, cytotoxic, Th1-like inflammatory, Th1-like cytotoxic and proliferative populations. Trajectory inference, TCR clonotype analysis and experimental Treg-to-exTreg conversion independently converged on this developmental framework, revealing that clonally related exTregs acquire distinct effector programs. Mechanistically, we identify Treg-intrinsic IL-6R signaling as an important driver of this process. IL-6 accelerated exTreg generation in vitro, whereas Treg-specific deletion of Il6ra reduced inflammatory exTreg differentiation and attenuated atherosclerosis in vivo. Together, these findings establish a framework for Treg destabilization during atherosclerosis and provide a conceptual basis for preserving Treg lineage stability in chronic inflammatory disease.

immunology↗

bioRxiv2026

Antibody Correlates of Resilience to Staphylococcus aureus Disease and Recurrence in Children

Staphylococcus aureus remains a major global pathogen with no licensed vaccine and high recurrent infection burden, yet correlates of protection remain undefined. In a prospective pediatric cohort, we profiled 319 children spanning non-carriers, asymptomatic carriers, those with skin and soft tissue infection (SSTI), or invasive disease. We interrogated 182,149 antibody features, generating the most comprehensive S. aureus immune profiling dataset to date. Antibody responses increased with age, marked by expansion of IgG subclasses and Fc-receptor engagement. Asymptomatic carriage was associated with functional antibody profiles targeting conserved surface antigens and select toxins. Multivariate modeling robustly distinguished clinical phenotypes and identified high-value antigens associated with disease resilience. Protection from recurrent disease converged on enhanced Fc{gamma}R binding and antibody effector function. These findings nominate key antigen targets, and highlight anti-Hla neutralizing antibodies and functional antibodies to additional surface antigens that can be recapitulated through Fc engineering, informing next-generation vaccine and monoclonal antibody strategies.

immunology↗

bioRxiv2026

scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data

Delineating biologically meaningful cell populations within single-cell embedding spaces requires methods that balance expert guidance with reproducibility. We present scDIG, a Shiny-based tool that integrates bimodal index-driven feature selection, feature-weighted kernel density estimation, and interactive contour-based gating to define cell populations directly within two-dimensional projections of scRNA-seq and CITE-seq data. We applied scDIG to CITE-seq PBMC data from human subjects in the Cardiovascular Assessment Virginia (CAVA) cohort and show that it resolves transcriptionally distinct CD4+ T cell subpopulations within continuous embeddings that are not readily captured by conventional clustering approaches. These findings demonstrate the utility of scDIG for robust, reproducible classification of single-cell populations and for identifying immunologically relevant effector states. The app is freely available for non-commercial use at https://au-cbgm-shiny.augusta.edu/gating, with source code available at https://gitlab.com/pbombina/scdig.

bioinformatics↗

bioRxiv2025

GOLF: A Generative AI Framework for Pathogenicity Prediction of Myocilin OLF Variants

Missense mutations in the MYOC gene, particularly those affecting the olfactomedin (OLF) domain of the myocilin protein, can be causal for open-angle glaucoma--a leading cause of irre-versible blindness. However, predicting the pathogenicity of these mutations remains challenging due to the complex effects of toxic gain-of-function variants and the scarcity of labeled clinical data. Herein, we present GOLF, a generative AI framework for assessing and explaining the pathogenicity of OLF domain variants. GOLF collects and curates a comprehensive dataset of OLF homologs and trains generative models that predict the effect of monoallelic missense mutations. While these models exhibit diverse predictive behaviors, they collectively achieve accurate classification of known pathogenic and benign variants. To interpret their decision mechanisms, GOLF uses a sparse autoencoder (SAE) that reveals the underlying biochemical features exploited by the generative models to predict variant effects. GOLF enables accurate evaluation of disease-causing mutations, supporting early genetic risk stratification for glaucoma and facilitating interpretable investigations into the molecular basis of pathogenic variants.

bioinformatics↗