Integrating metabolomics and transcriptomics reveals convergent pathways driving radiation-induced salivary gland dysfunction

Purpose: The goal of this study is to integrate NGS-derived parotid salivary gland transcriptome profiling (RNA-seq) to metabolomics (UPLC-MS/MS) methods and identify mechanisms driving radiation-induced xerostomia, with potential clinical application to head and neck cancer patients. Methods: Parotid salvary gland mRNA profiles of 28-day-old wild-type (WT) and 5 Gy radiation treated mice were generated by deep sequencing, in triplicate, using Illumina TruSeq. The sequenced reads that passed quality filters were trimmed with Trimmomatic, mapped to mm10 genome with HISAT2, and summarized at the gene level with HTSeq-count and analyzed with DESeq2. Results: Using an optimized data analysis workflow, we mapped sequenced reads per sample to the mouse genome (build mm10) and identified 25422 genes with at least 1 read in the parotid glands of WT and radiation treated mice with HISTA2 workflow. RNA-seq data QA/QC by Principal Component Analysis showed a clear separation between conditions. 155 genes showed significant differential expression between the WT and radiation treated salivary gland, with an adjsuted p-value < 0.05. Pre ranked Gene Set Enrichment Analysis against the CPDB database led to 859 altered pathways with a p-value < 0.05. Integrative analysis pointed to 103 RNA-Seq/Metabolite joint enriched pathways with a p-value < 0.05. Overall design: Parotid salivary gland mRNA profiles of 28-day old wild type (WT) and radiation treated mice