The transcriptomic revolution and radiation biology

Serum-Based Proteomic Approach to Identify Clinical Biomarkers of Radiation Exposure

BACKGROUND

Ionizing radiation (IR) exposure poses a significant health risk due to its widespread use in medical diagnostics and therapeutic applications, necessitating rapid and effective biomarkers for assessment.

OBJECTIVE

The aim of this study is to identify the serum proteomic signature of IR exposure in patients undergoing radiotherapy (RT).

METHODS

Blood samples were obtained from eighteen patients with head and neck cancer (HNC) and five patients with rectal cancer before and immediately after they underwent curative intensity-modulated radiotherapy (IMRT). The comprehensive serum proteome was analyzed in individual samples using nanoHPLC-MS/MS.

RESULTS

Forty radiation-modulated proteins (RMPs), 24 upregulated and 16 downregulated, with a fold change ≥1.5 and p-value < 0.05 were identified. About 40% of the RMPs are involved in acute phase response, DNA repair, and inflammation; the key RMPs were ADCY1, HGF, MCEMP1, CHD4, RECQL5, MSH6, and ZNF224. Conclusions: This study identifies a panel of serum proteins that may reflect the radiation response, providing a valuable molecular fingerprint of IR exposure and paving the way for the development of sensitive and specific biomarkers for early detection and clinical management of IR-related injuries.

FAILLA MEMORIAL LECTURE How We Got Here: One Laboratory's Odyssey in the Field of Radiation-Inducible Genes

This review focuses on early discoveries that contributed to our understanding and the scope of transcriptional responses after radiation damage. Before the development of modern approaches to assess overall global transcriptomic responses, the idea that mammalian cells could respond to DNA-damaging agents in a manner analogous to bacteria was not generally accepted. To investigate this possibility, the development of technology to identify differentially expressed low-abundance transcripts substantially facilitated our appreciation that DNA damaging agents like UV radiation and subsequently ionizing radiation did in fact produce robust transcriptional responses. Here we focus on our identification and characterization of radiation-inducible genes, and how even early studies on stress gene signaling highlighted the broad scope of transcriptional responses to radiation damage. Since then, the central role of transcriptional responses to radiation injury in maintaining genome integrity has been highlighted in many processes, including cell cycle checkpoint control, resistance to cancer by p53 and other key factors, cell senescence, and metabolism.

Small Bowel Dose Constraints in Radiation Therapy—Where Omics-Driven Biomarkers and Bioinformatics Can Take Us in the Future

Radiation-induced gastrointestinal (GI) dose constraints are still a matter of concern with the ongoing evolution of patient outcomes and treatment-related toxicity in the era of image-guided intensity-modulated radiation therapy (IMRT), stereotactic ablative radiotherapy (SABR), and novel systemic agents. Small bowel (SB) dose constraints in pelvic radiotherapy (RT) are a critical aspect of treatment planning, and prospective data to support them are scarce. Previous and current guidelines are based on retrospective data and experts’ opinions. Patient-related factors, including genetic, biological, and clinical features and systemic management, modulate toxicity. Omic and microbiome alterations between patients receiving RT to the SB may aid in the identification of patients at risk and real-time identification of acute and late toxicity. Actionable biomarkers may represent a pragmatic approach to translating findings into personalized treatment with biologically optimized dose escalation, given the mitigation of the understood risk. Biomarkers grounded in the genome, transcriptome, proteome, and microbiome should undergo analysis in trials that employ, R.T. Bioinformatic templates will be needed to help advance data collection, aggregation, and analysis, and eventually, decision making with respect to dose constraints in the modern RT era.

Transcriptome profile changes in the jejunum of nonhuman primates exposed to supralethal dose of total- or partial-body radiation

The risk of exposure of the general public or military personnel to high levels of ionizing radiation from nuclear weapons or radiological accidents is a dire national security matter. The development of advanced molecular biodosimetry methods, those that measure biological response, such as transcriptomics, to screen large populations of radiation-exposed victims is key to improving survival outcomes during radiological mass casualty scenarios. In this study, nonhuman primates were exposed to either 12.0 Gy cobalt-60 gamma (total-body irradiation, TBI) or X-ray (partial-body irradiation, PBI) 24 h after administration of a potential radiation medical countermeasure, gamma-tocotrienol (GT3). Changes in the jejunal transcriptomic profiles in GT3-treated and irradiated animals were compared to healthy controls to assess the extent of radiation damage. No major effect of GT3 on radiation-induced transcriptome at this radiation dose was identified. About 80% of the pathways with a known activation or repression state were commonly observed between both exposures. Several common pathways activated due to irradiation include FAK signaling, CREB signaling in the neurons, phagosome formation, and G-protein coupled signaling pathway. Sex-specific differences associated with excessive mortality among irradiated females were identified in this study, including Estrogen receptor signaling. Differential pathway activation was also identified across PBI and TBI, pointing towards altered molecular response for different degrees of bone marrow sparing and radiation doses. This study provides insight into radiation-induced changes in jejunal transcriptional profiles, supporting the investigation for the identification of biomarkers for radiation injury and countermeasure efficacy.

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction

Background

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias.

Aim

To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity.

Methods

A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search.

Results

Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R² of 0.97 and 0.99 for cortex and medulla, respectively.

Conclusion

The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

Effect of Age and Sex on Gene Expression-Based Radiation Biodosimetry Using Mouse Peripheral Blood

Blood-based gene expression profiles that can reconstruct radiation exposure are being developed as a practical approach to radiation biodosimetry. However, age and sex could potentially limit the accuracy of the approach. In this study, we determined the impact of age on the peripheral blood cell gene expression profile of female mice exposed to radiation and identified differences and similarities with a previously obtained transcriptomic signature of male mice. Young (2 months) and old (24 months) female mice were irradiated with 4 Gy X-rays, total RNA was isolated from blood 24 hours later and subjected to whole-genome microarray analysis. Dose reconstruction analyses using a gene signature trained on gene expression data from irradiated young male mice showed accurate reconstruction of 0 or 4 Gy doses with root mean square error of ±0.75 Gy (R2 = 0.90) in young female mice. Although dose reconstruction for irradiated old female mice was less accurate than young female mice, the deviation from the actual radiation dose was not statistically significant. Pathway analysis of differentially expressed genes revealed that after irradiation, apoptosis-related functions were overrepresented, whereas functions related to quantities of various immune cell subtypes were underrepresented, among differentially expressed genes from young female mice, but not older animals. Furthermore, young mice significantly upregulated genes involved in phagocytosis, a process that eliminates apoptotic cells and preserves tissue homeostasis. Both functions were also overrepresented in young, but not old, male mice following 4 Gy X-irradiation. Lastly, functions associated with neutrophil activation that is essential for killing invading pathogens and regulating the inflammatory response were predicted to be uniquely enriched in young but not old female mice. This work supports the concept that peripheral blood gene expression profiles can be identified in mice that accurately predict physical radiation dose exposure irrespective of age and sex.

Gene Expression Changes in a Prefinal Health Stage of Lethally Irradiated Male and Female Rhesus Macaques

Radiation-induced gene expression (GE) changes can be used for early and high-throughput biodosimetry within the first three days postirradiation. However, is the method applicable in situations such as the Alexander Litvinenko case or the Goiania accident, where diagnosis occurred in a prefinal health stage? We aimed to characterize gene expression changes in a prefinal health stage of lethally irradiated male and female rhesus macaques. Peripheral blood was drawn pre-exposure and at the prefinal stage of male and female animals, which did not survive whole-body exposure with 700 cGy (LD66/60). RNA samples originated from a blinded randomized Good Laboratory Practice study comprising altogether 142 irradiated rhesus macaques of whom 60 animals and blood samples (15 samples for both time points and sexes) were used for this analysis. We evaluated GE on 34 genes widely used in biodosimetry and prediction of the hematological acute radiation syndrome severity (H-ARS) employing quantitative real-time polymerase chain reaction (qRT-PCR). These genes were run in duplicate and triplicate and altogether 96 measurements per time point and sex could be performed. In addition, 18S ribosomal RNA (rRNA) was measured to depict the ribosome/transcriptome status as well as for normalization purposes and 16S rRNA was evaluated as a surrogate for bacteremia. Mean differential gene expression (DGE) was calculated for each gene and sex including all replicate measurements and using pre-exposure samples as the reference. From 34 genes, altogether 27 genes appeared expressed. Pre-exposure samples revealed no signs of bacteremia and 18S rRNA GE was in the normal range in all 30 samples. Regarding prefinal samples, 46.7% and 40% of animals appeared infected in females and males, respectively, and for almost all males this was associated with out of normal range 18S rRNA values. The total number of detectable GE measurements was sixfold (females) and 15-fold (males) reduced in prefinal relative to pre-exposure samples and about tenfold lower in 80% of prefinal compared to pre-exposure samples (P < 0.0001). An overall 11-fold (median) downregulation in prefinal compared to pre-exposure samples was identified for most of the 27 genes and even FDXR appeared 4-14-fold downregulated in contrast to a pronounced up-regulation according to cited work. This pattern of overall downregulation of almost all genes and the rapid reduction of detectable genes at a prefinal stage was found in uninfected animals with normal range 18S rRNA as well. In conclusion, in a prefinal stage after lethal radiation exposure, the ribosome/transcriptome status remains present (based on normal range 18S rRNA values) in 60-67% of animals, but the whole transcriptome activity in general appears silenced and cannot be used for biodosimetry purposes, but probably as an indicator for an emerging prefinal health stage.

Lung transcriptome of nonhuman primates exposed to total- and partial-body irradiation

The focus of radiation biodosimetry has changed recently, and a paradigm shift for using molecular technologies of omic platforms in addition to cytogenetic techniques has been observed. In our study, we have used a nonhuman primate model to investigate the impact of a supralethal dose of 12 Gy radiation on alterations in the lung transcriptome. We used 6 healthy and 32 irradiated animal samples to delineate radiation-induced changes. We also used a medical countermeasure, γ-tocotrienol (GT3), to observe any changes. We demonstrate significant radiation-induced changes in the lung transcriptome for total-body irradiation (TBI) and partial-body irradiation (PBI). However, no major influence of GT3 on radiation was noted in either comparison. Several common signaling pathways, including PI3K/AKT, GADD45, and p53, were upregulated in both exposures. TBI activated DNA-damage-related pathways in the lungs, whereas PTEN signaling was activated after PBI. Our study highlights the various transcriptional profiles associated with γ- and X-ray exposures, and the associated pathways include LXR/RXR activation in TBI, whereas pulmonary wound-healing and pulmonary fibrosis signaling was repressed in PBI. Our study provides important insights into the molecular pathways associated with irradiation that can be further investigated for biomarker discovery.