Causality of eosinophil cells and diabetic nephropathy: combined Single-cell RNA sequencing and Mendelian randomization analysis

Technical Summary: Causality of Eosinophil Cells and Diabetic Nephropathy

Problem Statement
Diabetic nephropathy (DN) is a severe microvascular complication of diabetes characterized by persistent albuminuria and declining kidney function, leading to significant morbidity and mortality. While inflammatory processes are recognized as key drivers of DN, the specific causal relationship between eosinophils—a type of blood leukocyte—and DN remains unconfirmed. Previous observational studies suggested a correlation between eosinophil counts and DN severity, but they could not definitively establish causality or elucidate the underlying molecular mechanisms at the single-cell level. This study aims to bridge this gap by investigating the causal link between eosinophils and DN and identifying the specific genes and regulatory mechanisms involved.

Methodology
The study employed a dual-approach strategy combining Mendelian Randomization (MR) with single-cell RNA sequencing (scRNA-seq) analysis:

1. Mendelian Randomization (MR):

   • Data Sources: Genome-wide association study (GWAS) data for eosinophil cell counts (ieu-b-33, n=563,946) and DN (finn-b-DM_NEPHROPATHY, n=213,746) were retrieved from the IEU Open GWAS and GWAS Catalog databases.
   • Instrumental Variables (IVs): 440 SNPs associated with eosinophil counts were selected as IVs (P < 5×10⁻⁸), filtered for linkage disequilibrium (r² < 0.001), and validated with an F-statistic > 10.
   • Analysis: Univariable MR (UVMR) was performed using Inverse Variance Weighted (IVW), MR Egger, weighted median, simple median, and maximum likelihood methods.
   • Sensitivity: Heterogeneity (Cochran's Q), horizontal pleiotropy (MR Egger intercept), and leave-one-out (LOO) analyses were conducted to ensure result robustness. Steiger directionality testing confirmed the causal direction.

2. Single-Cell RNA Sequencing (scRNA-seq) Integration:

   • Datasets: Bulk RNA-seq data (GSE30122) and scRNA-seq data (GSE131882) were obtained from the GEO database.
   • Processing: Standard quality control, normalization, and batch effect correction were performed using the Seurat package. Cell types were annotated based on marker genes.
   • Gene Identification: Differentially expressed genes (DEGs) were identified in bulk data (GSE30122) and eosinophil-specific sc-DEGs in single-cell data (GSE131882).
   • Intersection: The study intersected the 121 bulk DEGs with 153 eosinophil sc-DEGs to identify five key genes.
   • Functional Analysis: Gene Ontology (GO), KEGG pathway enrichment, Gene Set Variation Analysis (GSVA), and pseudotime trajectory analysis (Monocle2) were performed. Cell-cell communication was analyzed using CellChat.
   • Regulatory Network Construction: Competing endogenous RNA (ceRNA) networks (lncRNA-miRNA-mRNA) and transcription factor (TF) interactions were predicted using MicroCosm, TargetScan, starBase, and JASPAR databases, visualized via Cytoscape.

Key Results

• Causal Inference: MR analysis confirmed that eosinophil cell count is a causal risk factor for DN (IVW: P = 0.025, OR = 1.153, 95% CI: 1.018–1.306). Sensitivity analyses (heterogeneity, pleiotropy, LOO) supported the reliability of this finding, and Steiger testing confirmed the correct causal direction.
• Gene Identification:
  • 121 DEGs were identified in bulk DN samples, and 153 sc-DEGs were found in eosinophils.
  • Five Key Genes: Intersection analysis identified IL7R, CD53, BPTF, FNBP4, and ANKRD36B as central genes linking eosinophils to DN.
  • Expression Patterns: IL7R and CD53 were highly expressed in eosinophils; BPTF in epithelial cells; and FNBP4 and ANKRD36B in mesangial cells.
• Functional Enrichment:
  • The key genes were enriched in pathways including chemokine signaling, oxidative phosphorylation, cell adhesion, and endocytosis.
  • GSVA indicated that eosinophils in the DN group were enriched in downregulated pathways related to reactive oxygen species, angiogenesis, and epithelial-mesenchymal transition.
• Trajectory and Dynamics: Pseudotime analysis revealed that eosinophil counts remained consistently higher in the DN group across all developmental stages. Key gene expression decreased initially but increased significantly in the middle and late stages of eosinophil development in DN.
• Regulatory Networks:
  • miRNAs: 13 miRNAs were predicted to target the key genes (e.g., hsa-miR-17-5p targeting FNBP4 and ANKRD36B).
  • Transcription Factors: Multiple TFs were predicted to regulate the key genes, including GATA2 and STAT3 (regulating IL7R and CD53), PPARG (regulating FNBP4 and BPTF), and FOXC1/HOXA5 (regulating ANKRD36B, BPTF, and CD53).

Significance and Claims
The authors claim that this study provides the first evidence of a causal relationship between eosinophil counts and the development of diabetic nephropathy, moving beyond observational correlations. By integrating MR with scRNA-seq, the study identifies IL7R, CD53, BPTF, FNBP4, and ANKRD36B as potential key genes mediating this relationship.

The paper posits that these genes may construct a "gene-eosinophil-kidney injury" regulatory axis by influencing eosinophil proliferation, activation, adhesion, migration, and oxidative phosphorylation. This axis is hypothesized to exacerbate renal inflammatory injury and fibrosis, thereby driving DN progression. The findings offer new candidate targets for the diagnosis and treatment of DN, specifically highlighting the role of eosinophils in the immunopathological mechanisms of the disease.

Limitations
The authors acknowledge that the conclusions are primarily based on bioinformatics analysis and association verification. They note that prospective cohort studies and functional experiments are required to validate the predictive potential of these genes and to fully elucidate the underlying molecular mechanisms. The study also notes that the statistical approach of genetic aggregation limits the scope of analysis regarding interindividual differences.