Decoding Radiation-Induced Transcriptomic Signatures of Whole Blood Using Long-Read RNA-Seq: Clinical and Biodosimetric Implications
Salah, A.; Schmidberger, H.; Marini, F.; Zahnreich, S.
Show abstract
BackgroundGene expression profiling in radiation-exposed blood is a valuable tool for biodosimetry and clinical research. Evaluating the bloods transcriptomic radiation response provides insight into absorbed dose, hematotoxicities, and immune reactions. However, detailed analysis using long-read RNA sequencing is currently limited in its diffusion, despite the potential additional insights that could be extracted, including novel isoform discovery and on-the-field gene expression studies, owing to its portability. ResultsIn this study, we utilized Oxford Nanopore Technologies long-read RNA sequencing on human whole-blood samples from three healthy donors 6 hours after exposure to 4 Gy of X-rays. Compared to sham-irradiated (0 Gy) blood, gene-level differential expression analysis identified 117 upregulated and 66 downregulated genes, including canonical DNA damage repair and inflammatory responses. At the transcript level, 102 transcripts were significantly upregulated, and 17 were downregulated, revealing isoform-specific regulation that was not captured at the gene level. Notably, IL32, which showed no significant change at the gene level, exhibited strong upregulation of two transcript isoforms, while WDR74, ITM2B, AK2, and RPS19 displayed changes in transcript usage following irradiation. Leveraging the power of long-read RNA sequencing, we further identified 26 novel transcript isoforms, expanding the catalog of radiation-responsive transcripts. ConclusionsThis is the first comprehensive study of long-read RNA-seq for transcriptomic profiling of human whole blood following ionizing radiation. These findings highlight the ability of long-read RNA sequencing to provide a more detailed view of radiation-induced transcriptomic alterations, underscoring its potential for biodosimetry and clinical applications.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.