RNA-seq Analysis: TYK2 Role in Pancreatic Beta-Cell Development

Project Overview

This project reproduces the bioinformatic analysis from the publication "The type 1 diabetes gene TYK2 regulates β-cell development and its responses to interferon-α" by Chandra et al. (Nature Communications, 2022). The analysis focuses on RNA-seq data from wild-type versus TYK2 knockout cells at the S5 endocrine progenitor stage to understand TYK2's role in pancreatic beta-cell development.

Project Structure

rnaseq-project/
│
|-- data/ # Input data files
│ ├── final_counts.tsv # Raw count matrix
│ └── geneIDs.txt # Gene ID to symbol mapping
│
├── envs/ # Conda environment files
│ └── base_env.yml # Environment specification
│
├── modules/ # Nextflow modules
│ └── align/
│ └── concatenate/
│ └── fastqc/
│ └── index/
│ └── multiqc/
│ └── parse/
│ └── verse/
│
├── results/ # Analysis outputs
│ ├── significant_genenames_for_DAVID_*.txt # Gene lists for enrichment
│ ├── FGSEA_C2_results.tsv # FGSEA pathway results
│ ├── Reactome_Pathways_2024_table.txt # Reactome enrichment results
│ └── *.png # Generated figures
│
├── scripts/ # Analysis scripts
│ └── MainAnalysis.Rmd # Main analysis R Markdown file
│ └── concatenate_counts.py # Concatenate VERSE .exon.txt count files into a gene-by-sample matrix
│ └── parseGTF.py # Parse GTF files for gene ID and name
│
├── workflows/ # Nextflow workflows
│ ├── main.nf # Main Nextflow pipeline
│ └── nextflow.config # Nextflow configuration
│
├── RNAseq_Project_Report.pdf # Compiled PDF report
│
└── README.md

Key Findings

• Differential Expression: Identified 1,116 significant differentially expressed genes (48 upregulated, 100 downregulated with |log2FC| ≥ 1)
• Pathway Enrichment: TYK2 knockout affects extracellular matrix organization and beta-cell development pathways
• Quality Control: High-quality RNA-seq data with >92% alignment rates and clear separation in PCA
• Comparison to Original Study: Similar biological conclusions despite methodological differences in gene counts

Analysis Pipeline

1. Quality Control & Preprocessing

• FastQC and MultiQC for read quality assessment
• STAR alignment to GRCh38 reference genome
• VERSE for gene-level quantification
• CPM filtering (≥1 CPM in ≥3 samples)

2. Differential Expression Analysis

• DESeq2 for normalization and statistical testing
• Thresholds: padj < 0.05, |log2FC| ≥ 1
• Volcano plot visualization

3. Functional Enrichment

• Enrichr/Reactome: Over-representation analysis of significant DEGs
• FGSEA: Gene set enrichment analysis using ranked genes
• Both methods using C2 canonical pathways from MSigDB

4. Quality Assessment

• PCA and sample distance heatmaps
• Biological replicate consistency evaluation

Tools and Versions

• R 4.4.3 with Bioconductor packages
• DESeq2 1.50.0 (differential expression)
• FGSEA 1.32.4 (pathway enrichment)
• ggplot2 3.5.1 (visualization)
• FastQC 0.12.1 (quality control)
• STAR 2.7.11b (alignment)
• VERSE 0.1.5 (read counting)

Usage

To reproduce the analysis:

1. Ensure all required R packages are installed
2. Run the Project2Report.Rmd script in RStudio
3. The analysis will generate all figures and results in the results/ directory

Results Interpretation

The analysis confirms TYK2's critical role in regulating:

• Extracellular matrix organization pathways
• Beta-cell developmental transcription factors (NEUROG3, NKX2-2, INSM1)
• Cell signaling and motility pathways

These findings provide mechanistic insights into how TYK2 genetic variants may contribute to Type 1 Diabetes risk by disrupting beta-cell development.

Reference

Chandra, V., Ibrahim, H., Halliez, C. et al. The type 1 diabetes gene TYK2 regulates β-cell development and its responses to interferon-α. Nat Commun 13, 6363 (2022). https://doi.org/10.1038/s41467-022-34069-z