# 1-stearoyl-2-arachidonoyl-driven B Cell metabolic dysregulation in chronic rhinosinusitis with nasal polyps: insights from Mendelian randomization and single-cell RNA sequencing

**Authors:** Jian Wu, Yifang Sun, Shuyue Wang, Qian Zhang, Ying Lin, Caipeng Liu, Maomao Ai, Feng Yu, Lei Cao

PMC · DOI: 10.3389/fphar.2025.1719897 · Frontiers in Pharmacology · 2025-10-30

## TL;DR

This study explores how specific B cells and a lipid metabolite contribute to chronic rhinosinusitis with nasal polyps, revealing new insights into immune-metabolic interactions.

## Contribution

The study identifies 1-stearoyl-2-arachidonoyl and specific B cell subpopulations as novel risk factors for CRSwNP through integrated Mendelian randomization and single-cell RNA sequencing.

## Key findings

- HLA-DR expression on CD33− HLA-DR + B cells and the lipid metabolite 1-stearoyl-2-arachidonoyl are risk factors for CRSwNP.
- Specific B cell subpopulations show metabolic alterations linked to immune responses in CRSwNP.
- Ten key genes correlated with B cell regulatory functions were identified using machine learning.

## Abstract

Chronic rhinosinusitis with nasal polyps (CRSwNP), an inflammatory condition of unclear etiology, may involve immune dysregulation and metabolic alterations.

Utilizing Mendelian randomization, we investigated causal links between CRSwNP and profiles of 731 immune cell types and 1,400 metabolites. Single-cell RNA sequencing (scRNA-seq) was employed for cell type identification and transcription factor analysis. Metabolic profiling characterized cellular subpopulations, while Gene Set Enrichment Analysis (GSEA) and machine learning pinpointed key genes functionally linked to immune and inflammatory pathways (categorized via WGCNA and Metascape).

We identified expression of HLA-DR on CD33− HLA-DR + B cells and the lipid metabolite 1-stearoyl-2-arachidonoyl as risk factors for CRSwNP. scRNA-seq further revealed these specific B cell subpopulations exhibit metabolic levels linked to immune responses. Bulk RNA analysis confirmed upregulation of genes CD27 and DERL3, while machine learning identified a signature of ten key genes showing positive correlation with B cell regulatory functions.

This integrated study advances understanding of immune-metabolic crosstalk in CRSwNP pathogenesis, highlighting the role of metabolite-influenced B cell subsets in shaping the immune microenvironment, thereby suggesting novel therapeutic targets.

## Linked entities

- **Genes:** CD27 (CD27 molecule) [NCBI Gene 939], DERL3 (derlin 3) [NCBI Gene 91319]

## Full-text entities

- **Genes:** DERL3 (derlin 3) [NCBI Gene 91319] {aka C22orf14, IZP6, LLN2, derlin-3}, CD27 (CD27 molecule) [NCBI Gene 939] {aka S152, S152. LPFS2, T14, TNFRSF7, Tp55}, CD33 (CD33 molecule) [NCBI Gene 945] {aka CD33rSiglec, SIGLEC-3, SIGLEC3, p67}
- **Diseases:** CRSwNP (MESH:D009298), inflammatory (MESH:D007249)
- **Chemicals:** lipid (MESH:D008055), 1-stearoyl-2-arachidonoyl (-)

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12611945/full.md

## References

53 references — full list in the complete paper: https://tomesphere.com/paper/PMC12611945/full.md

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Source: https://tomesphere.com/paper/PMC12611945