Biology subjects

Sun, H.-W.

Publications and source records attributed to Sun, H.-W..

4 recordsLinked to original sources

bioRxiv2026

Branch-resolved IFN/STING-linked lesion-state architecture in psoriasis: multi-cohort derivation, held-out bulk replication and single-cell context analysis

Objective and designWe tested whether a frozen branch-resolved IFN/STING-linked lesion-state architecture derived from paired psoriasis transcriptomes would transport to an untouched bulk RNA-sequencing cohort and remain detectable within broad cell compartments. Material or subjectsSeven paired bulk cohorts formed the derivation backbone. GSE121212 provided held-out replication using 27 matched lesional/nonlesional pairs; treatment-facing cohorts and GSE228421 provided pharmacodynamic and cellular-context analyses. TreatmentNo intervention was administered by the authors; public datasets included ustekinumab-, etanercept-, secukinumab- and risankizumab-exposed samples. MethodsFrozen modules and branches were scored after cohort-wide gene standardization. GSE121212 counts underwent trimmed mean of M-values normalization and log2 counts-per-million transformation. Paired effects, upper-quartile STING-high contrasts, lesional Spearman coupling and prespecified rank concordance were evaluated. ResultsGSE121212 lesional-minus-nonlesional effects were 1.288 for STING-core, 1.478 for IFN-responsive activity and 1.188 for proximal-only STING; all lower 95% confidence limits exceeded zero. Coupling-vector concordance was strong (rho = 0.893, P = 0.0068), whereas STING-high effect concordance was moderate (rho = 0.536), yielding partial replication. ConclusionsHeld-out replication was partial: the IFN/STING lesion anchor and proximal/IFN-dominant coupling transported, while broader ordering did not fully transport. Transcriptomic findings do not establish biochemical STING activation, causality or clinical thresholds.

immunology↗

bioRxiv2025

Dysregulation of U12-Type Splicing in Lupus Neutrophils

ObjectiveNeutrophil dysfunction is a hallmark of systemic lupus erythematosus (SLE), but its molecular basis remains unclear. This study explores transcriptional and post-transcriptional changes in low-density granulocytes (LDGs), a proinflammatory neutrophil subset expanded in SLE, focusing on NADPH oxidase (Nox) function and minor intron splicing. MethodsLDGs and normal-density neutrophils (NDGs) were isolated from SLE patients and healthy controls (HCs). CYBA (P22phox) expression was evaluated at transcript and protein levels. Nox activity was measured using luminol assays. Bulk RNA sequencing and rMATS software were used to assess alternative splicing, particularly of U12-type intron-containing genes. ResultsCYBA expression was reduced in SLE LDGs (n=11) compared to SLE NDGs and HCs (n=6), with levels resembling those in chronic granulomatous disease neutrophils. SLE LDGs exhibited impaired Nox activity (n=7 SLE, n=12 HC). CYBA is a U12 intron-containing gene, and transcriptomic analysis revealed broad downregulation of this gene class in SLE LDGs, suggesting minor spliceosome dysfunction. rMATS analysis showed increased U12-type intron retention and widespread splicing defects-- including exon skipping and mutually exclusive exon use--in genes such as GBP5, MAEA and STX10. These abnormalities were validated in an independent long-read RNA-seq dataset from SLE PBMCs. Importantly, splicing disruptions correlated with disease activity and autoantibody profiles. ConclusionImpaired U12-dependent splicing may contribute to neutrophil dysfunction in SLE, potentially via defective oxidative burst and altered immune regulation. These findings highlight the minor spliceosome as a novel player in lupus pathogenesis.

immunology↗

bioRxiv2025

Preserved suppressive function despite loss of Foxp3: insights into the identity of regulatory T cells

Regulatory T (Treg) cells are essential for maintaining immune homeostasis, with Foxp3 acting as the master transcription factor governing their differentiation and function. The acquisition of effector signatures in Treg cells is closely tied to the surrounding tissue-specific immune environment and typically occurs alongside Foxp3 expression. In this study, we investigated the transcriptomic and functional consequences of Treg-mediated regulation in a Th2-driven disease setting. The application of both in vitro systems and in vivo disease models allowed us to mimic Th2-mediated environments. We could demonstrate Th2-driven loss of Foxp3 expression in Treg cells in vitro and in vivo. Transcriptomic analysis revealed a maintained Treg signature despite the loss of active Foxp3 expression. Functional characterization of Tregs both in vitro and in vivo uncovered a preserved suppressive capacity even in the absence of Foxp3. Our findings unveil that, despite loss of Foxp3, a preserved Treg signature remains intact enabling the regulation of Th2-mediated diseases. The persistence of this regulatory transcriptome highlights the importance for developing Treg-cell therapy strategies in cancer and autoimmune diseases independent of Foxp3 expression.

immunology↗

bioRxiv2023

RichPathR: a gene set enrichment analysis and visualization tool

Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologies generates the vast number of gene lists that are up or down regulated together. One way of getting meaningful insights of the relationship of these genes is utilizing existing knowledge bases linking them with biological functions or phenotypes. Multiple public databases with annotated gene sets are available for GSEA, and enrichR is the most popular web application still requiring custom tools for large-scale mining. richPathR package is a collection of R functions that helps researchers carry out exploratory analysis and visualization of gene set enrichment using EnrichR. AvailabilityThe package, test data and additional figures can be downloaded from https://github.com/niams-bdmds/richPathR.git. Contactsunh1@nih.gov

bioinformatics↗