Biology subjects

Yazdany, J.

Publications and source records attributed to Yazdany, J..

4 recordsLinked to original sources

bioRxiv2026

Longitudinal linear-epitope profiling of SLE identifies novel antigens associated with disease and disease activity

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disease defined by the production of autoantibodies directed against diverse self-antigens. Antinuclear antibodies (ANA) are central to SLE diagnosis, and anti-double-stranded DNA (anti-dsDNA) antibodies are clinically used to track disease activity. However, these biomarkers provide only partial insight into the full spectrum of autoimmune changes in patients. To expand the known landscape of SLE autoreactivity shared among patients and associated with disease activity, we performed an unbiased characterization of 1,268 SLE plasma samples from 695 SLE patients and 301 healthy controls using phage immunoprecipitation and sequencing (PhIP-seq). This technique preferentially displays linear epitopes, and thus provides a complementary set of autoreactivities to previously discovered autoreactive targets. Despite the marked clinical heterogeneity of SLE, machine learning applied to this data surfaced a shared, reproducible signature that robustly discriminates between SLE and healthy controls in two validation cohorts. Autoreactivities in SLE were significantly enriched for RNA-binding protein targets. Autoreactive proteins containing Basic Acidic Dipeptides (BAD) repeat domains, features associated with biomolecular condensate formation, distinguished patients with high disease activity (by SLEDAI score) from patients with no disease activity. Among all shared autoreactivities, a peptide derived from YTHDF2 emerged as the most prevalent target across SLE cohorts occurring in ~25% of SLE patients and displayed striking sequence similarity to a peptide derived from the Epstein Barr virus (EBV) lytic switch protein BZLF1. Competition assays demonstrated higher-affinity binding to the viral peptide, supporting a model in which antiviral humoral responses to EBV may drive secondary epitope spreading toward molecularly mimicked self-antigens. Remarkably, orthogonal validation using the Luminex bead platform containing a panel of only 10 peptides, including BZLF1, demonstrated strong discrimination between SLE and healthy controls (AUC = 0.95). Together, these findings identify a rich, greatly expanded landscape of autoreactivities in SLE, candidate biomarkers associated with high disease activity, and provide mechanistic insight linking EBV-driven immunity to epitope-specific autoimmunity in lupus.

immunology↗

bioRxiv2025

Transcriptomic analysis reveals immune signatures associated with specific cutaneous manifestations of lupus in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) presents with diverse heterogenous cutaneous manifestations. However, the molecular and immunologic pathways driving specific cutaneous manifestations of SLE are poorly understood. Here, we leverage transcriptomics from a large well-phenotyped longitudinal cohort of SLE patients to map molecular pathways linked to ten distinct SLE-related rashes. Through whole blood and immune cell-sorted bulk RNA sequencing, we identified immune signatures specific to cutaneous subtypes of SLE. Subacute cutaneous lupus (SCLE) exhibited broad upregulation of interferon, TNF-, and IL6-JAK-STAT3 pathways suggesting potential unique therapeutic responses to JAK and type I interferon inhibition. While interferon signaling is prominent in SCLE, discoid lupus, and acute lupus, it is unexpectedly attenuated in patients with skin and mucosal ulcers. Pathway and cell-type enrichment analysis revealed unexpected roles for CD14+ monocytes in photosensitivity of SLE and NK cells in alopecia, mucosal ulceration, and livedo reticularis. These findings illuminate the immune heterogeneity of rashes in SLE, highlighting subtype-specific mechanistic targets, and presenting opportunities for precision therapies for SLE-associated skin phenotypes.

bioinformatics↗

bioRxiv2023

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

molecular biology↗

bioRxiv2020

Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

bioinformatics↗