Biology subjects

Bodansky, A.

Publications and source records attributed to Bodansky, A..

5 recordsLinked to original sources

bioRxiv2026

AEGIS reveals epitope- and clone-resolved convergence of CNS B and T cell autoreactivity in ROHHAD

Autoimmune diseases arise when B and T lymphocytes lose tolerance to self. Yet in most disorders, the underlying molecular determinants, including autoantibodies, epitopes and lymphocyte clones that drive tissue injury remain undefined. Rapid-onset obesity with hypothalamic dysfunction, hypoventilation and autonomic dysregulation (ROHHAD) is a rare and often fatal pediatric neuroendocrine syndrome with strong evidence of antigen-driven paraneoplastic autoimmunity, including association with the intracellular autoantigen ZSCAN1. However, the effector immune circuit and the epitope-level determinants operating within the hypothalamus and brainstem have remained unknown. To address this challenge in ROHHAD and more broadly in autoimmune disease, we developed the Autoimmune Epitope and immunoGlobulin/Immune-receptor identification System (AEGIS), an integrated framework that links immune repertoires to their cognate self-epitopes. AEGIS combines B cell and T cell receptor profiling from sites of tissue injury with high-resolution epitope mapping, direct sequencing of antigen-specific autoantibodies, in silico antibody-antigen folding, selection, and T cell antigen discovery. Applied to a deeply phenotyped child with ROHHAD, AEGIS revealed a compartmentalized, clonally restricted immune response in which brain-deposited IgG and expanded cerebrospinal fluid B cell and CD4 T cell clonotypes converged on shared ZSCAN1 epitopes, resolved to minimal determinants and peptide-MHC ligands. These findings provide a clone- and epitope-linked mechanistic map of ROHHAD autoimmunity and establish a generalizable framework for identifying candidate pathogenic clones and antigens across diverse autoimmune diseases.

immunology↗

bioRxiv2026

ORION: An agentic reasoning construct for the analysis of complex human immune profiling

The capacity to generate high-dimensional biological datasets has outpaced the ability to interpret them. Technologies such as phage immunoprecipitation and sequencing (PhIP-seq) enable proteome-scale profiling of antibody repertoires, but interpreting thousands of enriched peptides into mechanistic hypotheses remains a labor-intensive bottleneck requiring expert synthesis of statistics, literature, and domain knowledge. Here we describe ORION (Omics Reasoning & Interpretation Orchestrator), a multi-agent framework that uses reasoning-capable large language models to perform end-to-end analysis of complex immune profiling data. ORION integrates statistical analysis, machine learning, and automated literature review into a single structured workflow, producing results that are reproducible and fully traceable. Applied to a published PhIP-seq dataset from autoimmune polyendocrine syndrome type 1 (APS-1), ORION recovered the canonical autoantibody signature in approximately two hours, closely recapitulating an analysis that originally required one to two months of manual effort. To test hypothesis-generation capacity on previously unseen data, we applied ORION to a novel PhIP-seq dataset from individuals with Down syndrome, for which no proteome-wide autoantibody reference exists. ORION distinguished disease from control samples with high accuracy, prioritized candidate autoantibody targets, and organized them into biologically coherent groups spanning immune, gut, and neuronal programs, generating testable hypotheses for experimental follow-up. These results demonstrate that agentic AI systems can compress the analysis of complex immune profiling data from weeks to hours, allowing scientists to redirect their time toward the fundamental biology.

bioinformatics↗

bioRxiv2026

Structure-guided design of a targeted autoantibody degrader for neurologic disease

Despite rapid progress in the diagnosis of autoantibody-mediated neurologic diseases, standard-of-care therapeutic options remain limited to nonspecific immunosuppression. Here, we report an alternative therapeutic strategy using targeted protein degradation to eliminate pathogenic autoantibodies while leaving the rest of the immune system intact. We previously discovered autoimmune vitamin B12 central deficiency (ABCD), a neurologic condition in which autoantibodies targeting the transcobalamin receptor (CD320) impair the transport of cobalamin (B12) from the blood into the central nervous system (CNS). Combining scanning alanine mutagenesis by phage display, cryo-electron microscopy, and computational modeling, we elucidated a highly conserved anti-CD320 epitope and defined the structural determinants of antigen-autoantibody binding. Next, we synthesized a lysosome-targeting chimera (LYTAC) comprising the lysosome targeting glycan, triGalNAc, fused to the antigenic epitope of CD320 as autoantibody bait. In vitro, this LYTAC promoted the specific lysosomal internalization and extracellular clearance of anti-CD320, restoring homeostatic cellular uptake of B12. In a passive transfer mouse model of ABCD, LYTAC treatment rapidly cleared anti-CD320 from circulation and prevented penetration of anti-CD320 into the CNS. These findings uncover the mechanism of autoantibody-antigen binding in ABCD and demonstrate targeted autoantibody degradation as a therapeutic strategy that may be generalizable to other autoimmune neurologic diseases.

neuroscience↗

bioRxiv2024

Microvascular preservation and cardiomyocyte hyperplasia underlie adaptive right ventricular development in congenital heart disease-associated pulmonary arterial hypertension

AbstractO_ST_ABSBackgroundC_ST_ABSRight ventricular (RV) failure is the primary cause of death among patients with pulmonary arterial hypertension (PAH). Patients with congenital heart disease- associated PAH (CHD-PAH) demonstrate improved outcomes compared to patients with other forms of PAH, which is related to the maintenance of an adaptively hypertrophied RV. In an ovine model of CHD-PAH, we aimed to elucidate the cellular, microvascular, and transcriptional adaptations to congenital pressure overload that support RV function in CHD-PAH. MethodsFetal surgery was performed on late gestation lambs to insert a large aortopulmonary graft, leading to a persistent congenital left-right shunt and RV pressure load. At 3 days and 4-6 weeks of life, shunt RV microvasculature, cardiomyocyte structure, and myocardial growth mechanisms were compared to age-matched controls and unoperated fetal RV. RNA sequencing was performed to assess differences in the RV transcriptomes. ResultsAt 4-6 weeks of age, shunt lambs demonstrate significant RV enlargement (shunt 37.1 {+/-} 2.9g vs control 15.9 {+/-} 1.0g, p<0.001) but maintain stable microvascular density (fetal 3.0 {+/-} 0.6 vs shunt 2.9 {+/-} 0.3 vs control 3.1 {+/-} 0.6 capillaries per 1000 {micro}m3, p>0.05). Shunt RV cardiomyocytes are significantly smaller by cross-sectional area and more numerous than age-matched controls (shunt 73.3 {+/-} 5.5 {micro}m2 vs control 99.2 {+/-} 4.9 {micro}m2, p=0.013). At 3 days, shunt RV cardiomyocytes show evidence of increased proliferative capacity and ongoing hyperplasia compared to controls. RNA sequencing analyses reveal a distinct gene expression profile in shunt RV consistent with a delay in terminal differentiation and metabolic adaptations to support adaptive function. ConclusionsThis study provides novel insights into the development of adaptive RV hypertrophy in CHD-PAH, demonstrating roles for preserved microvascular density and increased postnatal cardiomyocyte hyperplasia in supporting RV performance. Future investigations into the mechanisms underlying these changes could have significant implications for the development of novel therapeutic strategies for supporting RV function.

physiology↗

bioRxiv2022

Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq

Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.

immunology↗