# A multi-omics analysis of pancreatitis: bridging familial genetics and disease progression

## TL;DR

This study combines genetic and immune cell analysis to uncover shared mechanisms in pancreatitis, linking rare genetic mutations to immune responses and disease severity.

## Contribution

The study integrates familial genetics with immune profiling to reveal novel genes and immune signatures in pancreatitis.

## Key findings

- Heterozygous mutations in 12 novel genes, including EXOC4, ATG2A, and UNC80, were identified in familial chronic pancreatitis.
- Altered immune cell profiles, including increased naive B cells and CD8+ T cells, were observed in both familial and acute pancreatitis.
- An AI-driven model using 110 CP-related genes predicted AP severity with high accuracy and identified 17 potential biomarker genes.

## Abstract

Chronic and acute pancreatitis (CP and AP, respectively) are debilitating conditions with significant morbidity and mortality, necessitating a comprehensive understanding of their underlying mechanisms. This study provides a high-resolution, multi-omics investigation into the genetic and immune cell underpinnings of pancreatitis, integrating rare familial CP with a large cohort of patients with AP. Utilizing an integrative approach that combined whole-exome sequencing (WES) from two pediatric CP patients and their family members with single-cell RNA sequencing (scRNA-seq) and bulk transcriptomics from a public AP cohort (n = 119), we identified a shared molecular and cellular pathology. WES of the CP family revealed heterozygous mutations in 12 novel genes, including EXOC4, ATG2A, and UNC80. Functional enrichment analysis highlighted autophagy, cell adhesion, and vesicle-mediated transport as the key biological processes implicated in the pathophysiology of both conditions. Single-cell profiling of peripheral blood mononuclear cells (PBMCs) from the CP family revealed a marked increase in the proportion of naive B cells and an altered activity of CD8+ T cells, suggesting a dysregulated B-cell-mediated immune response. This observation was corroborated in the AP cohort, where CIBERSORT analysis revealed a significant increase in both naive B cells and CD8+ T cells correlating with the disease severity. Weighted gene co-expression network analysis (WGCNA) on the AP cohort uncovered 14 gene modules associated with disease progression. These modules were significantly enriched for pathways central to the innate immune response, including complement-dependent cytotoxicity and neutrophil degranulation, providing a molecular link to the observed immune cell infiltration. An artificial intelligence (AI)-driven model incorporating 110 CP family-related genes (GTCPFs) demonstrated exceptional predictive capability (average AUC > 0.84) for AP severity, highlighting the translational potential of our findings. The model identified a robust signature of 17 genes, including ATG2A, EXOC4, and TNS1, which may serve as novel diagnostic and prognostic biomarkers. Our findings provide a unified view of the pathogenesis of pancreatitis, linking novel genetic variants to specific immune cell and transcriptomic signatures. This integrative approach underscores the critical importance of both genetic and immune factors in CP and AP, identifying potential biomarkers and therapeutic targets and paving the way for personalized medicine in the management of these challenging conditions.

Flowchart illustrating a genetic and cellular analysis workflow. The left column represents processes for Family CP, including family whole exome sequencing, single cell sequencing, cell differentiation potency, cell communication, and signature validation. The right column represents processes for Healthy/Mild/Moderate_Severe/Severe AP, including AI-driven analysis, functional enrichment, WGCNA, immune cell infiltration and correlation, and signature validation. Visuals include diagrams of cell sequencing results, heatmaps, and various plots supporting each step.