SpliceSD: A Transcriptome-Wide Analysis of Alternative Splicing in Sjögren's Disease

1. Overview

The molecular mechanisms driving Sjögren's disease (SD), a complex autoimmune disorder, remain poorly understood. This project will investigate the role of alternative mRNA splicing, a plausible but underexplored pathogenic mechanism, by conducting a comprehensive bioinformatic analysis of public RNA-seq datasets. Our primary goal is to create the first transcriptome-wide map of splicing events in SD, identifying disease-specific patterns that could serve as biomarkers or reveal novel therapeutic targets. An additional deliverable will be an interactive Quarto report to make our findings easily accessible.

The project is planned for a duration of 6 months:

2 months for data acquisition and pipeline execution
3 months for downstream analysis and interpretation
1 month for reporting and manuscript preparation

This project will be conducted using the computational resources at the Federal University of Rio Grande do Norte (UFRN). All analysis scripts will be made available on GitHub, and the final results will be archived on Zenodo for public access.

2. Introduction

Sjögren's disease (SD) is a chronic, systemic autoimmune disorder defined by a complex and heterogeneous clinical presentation. While historically characterized by its effects on exocrine glands, a contemporary understanding recognizes SD as a systemic condition with a wide spectrum of potential organ involvement and a pathophysiology rooted in the dysregulation of both innate and adaptive immunity [1], [2]. Despite significant progress, the specific molecular events that initiate this epithelial-targeted immune response and drive its systemic progression remain poorly understood.

A critical, yet underexplored, mechanism that could contribute to the pathogenesis of SD is splicing. This fundamental cellular process allows for the generation of multiple distinct mRNA isoforms from a single gene. While there have been studies pointing to splicing-related phenomena influencing the pathogenesis of SD, such as SD-risk loci in splicing regulatory regions of genes OAS1 and GTF2I [3], [4], there hasn’t been a transcriptome-wide assessment and no such data resource exists.

This project proposes a comprehensive bioinformatic analysis of publicly available transcriptomic datasets from SD patients and healthy controls to investigate the role of splicing. By leveraging these data, we aim to identify disease-associated differential splicing events and aberrant isoform expression and isoform usage patterns in affected tissues. The central objective is to map these splicing signatures to specific genes and pathways, providing novel insights into the molecular mechanisms that underpin SD pathogenesis.

Hypotheses

H1: Sjögren's disease patients exhibit a distinct, tissue-specific alternative splicing signature in affected epithelial and infiltrating immune cells compared to healthy controls.

H2: Disease-associated splicing isoforms will be enriched in genes integral to autoimmune pathogenesis, including those involved in antigen presentation, innate immune signaling, lymphocyte activation, and apoptosis.

Impact Statement

This research will provide the first comprehensive, transcriptome-wide map of alternative splicing in SD. The successful identification of these signatures could yield a new class of candidate biomarkers for improved diagnosis and patient stratification. This project remains significant even if our primary hypotheses are not fully supported. Should we find no widespread, disease-associated splicing signature, this result would strongly suggest that dysregulated alternative splicing is not a central driver of SD pathogenesis, thereby shining a new light on the understudied disease.

3. Methods

1. Data Acquisition and Pre-processing:

Publicly available RNA-seq datasets from the salivary gland of SD patients and healthy controls will be downloaded from the NCBI Sequence Read Archive (SRA) database (Table 1).

Study Accession	Number of SD samples	Number of Controls
SRP279407 [5]	9	8
SRP287550 [6]	16	13
ERP129369 [7]	23	-

Table 1: Tentative table of datasets to be used to conduct the analysis.

2. Alternative Splicing Analysis:

The core analysis will be performed using the nf-core/rnasplice pipeline [8]. This pipeline will be used to conduct a comprehensive transcriptome-wide analysis, including the quantification of alternative splicing events (ASEs), differential exon usage (DEU), and differential transcript usage (DTU) between SD and control samples.

3. Functional Enrichment Analysis:

To understand the biological implications of the identified splicing changes, we will perform functional enrichment analysis. Gene sets showing significant differential splicing or transcript usage will be analyzed using the R package clusterProfiler [9].

4. Visualization and Reporting:

All methods, results, and visualizations will be compiled into an interactive Quarto report, ensuring a transparent, reproducible, and easily shareable summary of our findings.

Planned Analyses

All computational infrastructure will be provided by the High-performance computing center at UFRN (NPAD/UFRN)

We will:

Assess alternative splicing events in SD.
Analyze differential transcript usage and differential exon usage in SD.
Make these results available through an interactive Quarto report.

Ethics and Data Management

No new data collection will occur. All data used are publicly available. Data processing will comply with FAIR principles, and all scripts will be open-source (GitHub). Results will be archived on Zenodo.

Figure 1: Overview of the methodology to be applied in this pro

4. Budget

Item	Item Estimated Cost (USD)
Open-Access publication fees*	$2,590
Stipend for a part-time research assistant**	$2,000
Additional costs	$410
Total	$5,000

* Scientific Reports or similar journal
** According to usual CAPES/CNpq rates in Brazil

6. References

[1] C. P. Mavragani and H. M. Moutsopoulos, “Sjögren’s syndrome: Old and new therapeutic targets,” J. Autoimmun., vol. 110, p. 102364, Jun. 2020, doi: 10.1016/j.jaut.2019.102364.

[2] C. Baldini, L. G. Chatzis, G. Fulvio, G. La Rocca, E. Pontarini, and M. Bombardieri, “Pathogenesis of Sjögren’s disease: one year in review 2024,” Clin. Exp. Rheumatol., vol. 42, no. 12, pp. 2336–2343, Dec. 2024, doi: 10.55563/clinexprheumatol/i8iszc.

[3] H. Li et al., “Identification of a Sjögren’s syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons,” PLOS Genet., vol. 13, no. 6, p. e1006820, Jun. 2017, doi: 10.1371/journal.pgen.1006820.

[4] C. Luo et al., “Risk Allele rs117026326-Mediated Alternative Splicing of GTF2I Promotes B Cell Proliferation in Primary Sjögren’s Syndrome,” J. Immunol. Res., vol. 2025, p. 4821639, Feb. 2025, doi: 10.1155/jimr/4821639.

[5] A. Oyelakin et al., “Transcriptomic and Network Analysis of Minor Salivary Glands of Patients With Primary Sjögren’s Syndrome,” Front. Immunol., vol. 11, Jan. 2021, doi: 10.3389/fimmu.2020.606268.

[6] J. Luo et al., “Transcriptome Sequencing Reveals Potential Roles of ICOS in Primary Sjögren’s Syndrome,” Front. Cell Dev. Biol., vol. 8, p. 592490, 2020, doi: 10.3389/fcell.2020.592490.

[7] E. Pontarini et al., “NKp30 Receptor Upregulation in Salivary Glands of Sjögren’s Syndrome Characterizes Ectopic Lymphoid Structures and Is Restricted by Rituximab Treatment,” Front. Immunol., vol. 12, p. 706737, Sep. 2021, doi: 10.3389/fimmu.2021.706737.

[8] J. Ashmore et al., nf-core/rnasplice: nf-core/rnasplice 1.0.4. (May 09, 2024). Zenodo. doi: 10.5281/zenodo.15194198.

[9] S. Xu et al., “Using clusterProfiler to characterize multiomics data,” Nat. Protoc., vol. 19, no. 11, pp. 3292–3320, Nov. 2024, doi: 10.1038/s41596-024-01020-z.

SpliceSD: A Transcriptome-Wide Analysis of Alternative Splicing in Sjögren's Disease

SpliceSD: A Transcriptome-Wide Analysis of Alternative Splicing in Sjögren's Disease

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