Biology subjects

Magis, A.

Publications and source records attributed to Magis, A..

4 recordsLinked to original sources

bioRxiv2026

The microbial soy metabolite S-equol mitigates cadmium neurotoxicity in mice

Environmental exposures are major but understudied determinants of chronic disease. Cadmium is a pervasive toxic metal encountered through diet and tobacco smoke that has been associated with cognitive impairment and Alzheimer's disease mortality, yet strategies to mitigate its neurotoxicity remain limited. S-equol is a gut microbial metabolite of soy daidzein that crosses the blood brain barrier and preferentially activates estrogen receptor beta. Here, we show that cadmium disrupts an axis linking the equol producing gut bacterium Adlercreutzia equolifaciens to circulating S-equol and hippocampal estrogen receptor expression in mice, that S-equol attenuates cadmium-induced cytotoxicity and restores neuronal, glutamatergic and synaptic transcriptional programs in mouse neural progenitor cells, and that circulating S-equol in older humans is lower in apolipoprotein E4 carriers and covaries with body mass index, identifying S-equol as a microbiome-derived, host-modulated candidate countermeasure against cadmium neurotoxicity.

neuroscience↗

bioRxiv2026

EBV Reprograms B Cells in an Autoimmune-Like Fashion in Patients with COVID-19

Epstein-Barr virus (EBV) reprograms B cells in autoimmune disease. Reprogrammed EBV+ B cells activate nearby B and CD4+ T cells, via upregulated antigen presentation and costimulatory machinery, to drive autoimmune pathology. EBV reactivation is a known correlate of long COVID, which is a heterogeneous condition that can bear similarities to autoimmune disease. However, the mechanisms underpinning this association remain unresolved. We report on EBV metabolically reprogrammed B cells in patients with COVID-19. We find EBV+ B cells provide stimulatory signals to bystander B and CD4+ T cells. SARS-CoV-2 infected participants exhibiting elevated fractions of EBV+ B cells present, at convalescence, with dysregulated lipid profiles, increased autoantibody titers, and post-acute symptomology likely reflective of this metabolic reprogramming and cell-cell interactions. Enrichment of our EBV+ B cell signatures seen in patients with COVID-19 is similar in patients with lupus and multiple sclerosis suggesting a potentially shared pathway of EBV-driven dysfunction across diseases.

immunology↗

bioRxiv2026

Modeling healthy proteomic profiles for anomaly detection using subspace learning based one-class classification

High-throughput plasma proteomics provides sensitive and scalable measurements of thousands of systemic protein profiles from minimally invasive blood samples, creating new opportunities for disease detection and population-scale health monitoring. However, robust statistical modeling remains challenging due to high dimensionality, limited availability and high diversity of diseased samples, resulting in class imbalance in clinical cohorts. Here, we present a subspace One-Class Classification (OCC) framework for proteomics-driven anomaly detection that models healthy proteomic profiles as a reference distribution. To address the limitations of conventional hyperparameter tuning in severely imbalanced data settings, we introduce a fully data-driven parameter estimation strategy that infers all model parameters directly from intrinsic properties of the healthy training data, without using any disease labels. Using plasma proteomics data generated with Olink, we evaluate a family of subspace and graph-embedded subspace extensions of Support Vector Data Description, in which all models operate on learned low-dimensional representations rather than the original feature space. Models are trained exclusively on a healthy reference cohort and evaluated on heterogeneous disease conditions, including multiple cancer types and an independent COVID-19 cohort, with all disease samples withheld from training to enable unbiased assessment of cross-disease generalization. Across disease contexts, the evaluated one-class models yield stable and balanced detection performance, demonstrating that learning structured low-dimensional representations of healthy proteomic variation captures intrinsic biological organization that generalizes across disease-specific perturbations. These results establish healthy-profile-based, subspace one-class learning as a robust and disease-agnostic framework for screening in high-dimensional plasma proteomics.

bioinformatics↗

bioRxiv2020

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗