Ionizing radiation elicits complex cellular responses that are influenced by both total dose and delivery rate. Understanding how dose rate modulates molecular outcomes is important for accurate risk assessment. In this study, we apply an integrative multi-omics approach combining transcriptomic and proteomic profiling, adjusting for covariates, to investigate how differential dose rates alter gene and protein expression in human lymphocytes, with emphasis on alterations in key molecular pathways. Methods: Peripheral blood from 14 healthy donors (8 males, 6 females) was irradiated ex vivo with X-rays at 0.05 Gy/minute (DR1) and 1.0 Gy/minute (DR2) across a dose range 0-6 Gy. Gene expression was assessed using TempO-Seq™, and relative protein abundance was determined by mass spectrometry. Differential expression analysis was conducted using edgeR and limma, adjusting for sex, age, and leukocyte counts (false discovery rate (FDR) < 0.05). Multi-omics integration was performed using regularized canonical correlation analysis (rCCA) implemented in mixOmics, followed by Reactome pathway enrichment analysis. Results: We identified 2,477 and 2,612 differentially expressed genes at DR1 and DR2, respectively, and 368 and 386 differentially expressed proteins. Using canonical variates from rCCA, we show that covariate adjustment improved dose discrimination, particularly above 0.5 Gy. Using a correlation cut-off threshold of 0.5 in rCCA, 212 (DR1) and 276 (DR2) highly correlated gene-protein pairs were identified. DR2 exposure was associated with stronger enrichment of stress-related pathways, including unfolded protein response, senescence and oncogenic kinase signaling. In contrast, DR1 induced enrichment of pathways associated with immune engagement, including antigen presentation. At both dose rates, transcriptomic changes highlighted upstream regulatory processes (chromatin modeling) and proteomic changes captured downstream functional pathways such as immune activity and apoptosis. Conclusion: Multi-omics approach with covariate adjustment revealed key radiation-responsive pathways and dose-rate-dependent molecular differences, highlighting the value of integrating transcriptomic and proteomic data to better understand radiation effects.
Subedi et al. (Mon,) studied this question.