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8 May 2024 Validating Radiosensitivity with Pre-Exposure Differential Gene Expression in Peripheral Blood Predicting Survival and Non-Survival in a Second Irradiated Rhesus Macaque Cohort
D. Schwanke, O. O. Fatanmi, S. Y. Wise, P. Ostheim, S. Schüle, G. Kaletka, S. Stewart, T. Wiegel, V. K. Singh, M. Port, M. Abend
Author Affiliations +
Abstract

Radiosensitivity differs in humans and possibly in closely related nonhuman primates. The reasons for variation in radiosensitivity are not well known. In an earlier study, we examined gene expression (GE) pre-radiation in peripheral blood among male (n = 62) and female (n = 60) rhesus macaques (n = 122), which did or did not survive (up to 60 days) after whole-body exposure of 7.0 Gy (LD66/60). Eight genes (CHD5, CHI3L1, DYSF, EPX, IGF2BP1, LCN2, MBOAT4, SLC22A4) revealed significant associations with survival. Access to a second rhesus macaque cohort (males = 40, females = 23, total n = 63) irradiated with 5.8–7.2 Gy (LD29-50/60) and some treated with gamma-tocotrienol (GT3, a radiation countermeasure) allowed us to validate these gene expression changes independently. Total RNA was isolated from whole blood samples and examined by quantitative RT-PCR on a 96-well format. cycle threshold (Ct)-values normalized to 18S rRNA were analyzed for their association with survival. Regardless of the species-specific TaqMan assay, similar results were obtained. Two genes (CHD5 and CHI3L1) out of eight revealed a significant association with survival in the second cohort, while only CHD5 (involved in DNA damage response and proliferation control) showed mean gene expression changes in the same direction for both cohorts. No expected association of CHD5 GE with dose, treatment, or sex could be established. Instead, we observed significant associations for those comparisons comprising pre-exposure samples with CHD5 Ct values ≤ 11 (total n = 17). CHD5 Ct values ≤ 11 in these comparisons were mainly associated with increased frequencies (61-100%) of non-survivors, a trend which depending on the sample numbers, reached significance (P = 0.03) in males and, accordingly, in females. This was also reflected by a logistic regression model including all available samples from both cohorts comprising CHD5 measurements (n = 104, odds ratio 1.38, 95% CI 1.07–1.79, P = 0.01). However, this association was driven by males (odds ratio 1.62, 95% CI 1.10–2.38, P = 0.01) and CHD5 Ct values ≤ 11 since removing low CHD5 Ct values from this model, converted to insignificance (P = 0.19). A second male subcohort comprising high CHD5 Ct values ≥ 14.4 in both cohorts (n = 5) appeared associated with survival. Removing these high CHD5 Ct values converted the model borderline significant (P = 0.051). Based on the probability function of the receiver operating characteristics (ROC) curves, 8 (12.3%) and 5 (7.7%) from 65 pre-exposure RNA measurements in males, death and survival could be predicted with a negative and positive predictive value ranging between 85–100%. An associated odds ratio reflected a 62% elevated risk for dying or surviving per unit change (Ct-value) in gene expression, considering the before-mentioned CHD5 thresholds in RNA copy numbers. In conclusion, we identified two subsets of male animals characterized by increased (Ct values ≤ 11) and decreased (Ct values ≥ 14.4) CHD5 GE copy numbers before radiation exposure, which independently of the cohort, radiation exposure or treatment appeared to predict the death or survival in males.

INTRODUCTION

Inter-individual differences in radiation resistance and radiation sensitivity (“radiosensitivity”) after exposure to ionizing radiation is important to study. Nevertheless, the nature of the underlying variation in radiosensitivity among individuals is poorly understood.

It is well known that ionizing radiation is described as a two-edged sword (1). On the one hand, ionizing radiation is well known for its potential to control tumor cell growth in radiotherapy. On the other hand, its potential to cause stochastic (e.g., tumors) and deterministic health effects like the (hematopoietic) acute radiation syndrome (H-ARS) has long been known. In the latter case, for instance, with a whole-body exposure of 3–4 Gy (LD50/60), about 50% of humans will die from ARS within 60 days without treatment (2). Knowing that this dose provides a 50% chance of predicting the clinical outcome, which underlines the complexity of this issue. The relation of exposure to radiation with ARS depends on several factors, including radiation exposure characteristics (e.g., radiation quality, fractionation, dose rate, partial-/total-body exposure), inherent biological processes (e.g., cell cycle dependency, oxygenation) (3), as well as other aspects such as radiosensitivity or pre-exposure health conditions. We hypothesize that the individual transcriptional makeup of a person at the time of the radiation exposure may determine radiation sensitivity (or resistance) of individuals.

Understanding the individual human responses to radiation exposure with respect to tissue damage or developing radiation-related sequelae would be of benefit in several instances. For example, manned space projects like the ARTEMIS mission would prosper from knowing the individual radiosensitivity in selecting potential astronauts (4). Furthermore, the diagnostic windows in radiotherapy could be widened, and individual specific radiotherapy might be applied when the radiosensitivity of patient's tumors and normal tissue would be predicted a priori (57). Concerning preparedness against radiological/nuclear (RN) mass casualties, such as dirty-bomb or atomic-bomb scenarios, individuals exposed to the same magnitude of radiation might develop different degrees of life-threatening ARS. Knowing about individual radiosensitivity could guide the treating physicians in choosing the appropriate therapy (8). Hence the question arises whether the transcriptional status of cells prior to radiation exposure influences the degree of radiation damage or H-ARS severity?

There are indications that the radiation-induced cancer risk differs among females and males (9, 10), and between some ethnicities, as shown, e.g., with breast or prostate cancer (11, 12). Animal studies suggest a difference in sex in the response to certain dose regarding hematologic acute radiation syndrome (1315).

In previous work in a male baboon model, several promising micro-RNAs (miRNA) addressing the H-ARS endpoint could be detected (16). miRNA has also been investigated using male and female rhesus macaques and total-body gamma irradiation. Results of this study suggested that female rhesus macaques may to be more sensitive to radiation, but the difference was not significant (17). In a further study, radiosensitivity with the endpoint survival and non-survival after total-body irradiation (TBI) in 122 male and female rhesus macaques (18) was evaluated. Hereby, eight candidate genes associated with survival could be independently validated.

In collaboration with Armed Forces Radiobiology Research Institute (AFRRI), we accessed a second rhesus macaque cohort comprising pre-exposure blood samples of 63 male and female rhesus macaques. The individual animals were exposed to 5.8–7.2 Gy of radiation (TBI) corresponding to ∼LD30–70/60. These nonhuman primates (NHP) were either untreated (placebo) or treated with different doses of a potential medical countermeasure, gamma-tocotrienol (GT3) (1923). These rhesus macaques enabled an independent validation of eight already identified radiosensitivity (survival) predicting genes in a second cohort. Due to the close homology of the human and rhesus macaque species, in addition to the well-characterized human transcriptome, a gene detection assay based on the human genome was employed in rhesus macaques in the previous study. As well as these assays, complimentary gene detection assays using the rhesus macaque transcriptome were generated and examined within this validation study. We hypothesized that the pre-exposure transcription status of the cells at the time of exposure would impact survival, thus, reflecting differences in radiosensitivity (identifying non-survivor) or radioresistance (identifying survivor). This is probably the most robust parameter in nature: cell death or, regarding an organism, survival and non-survival.

MATERIALS AND METHODS

Animals and Irradiation

Rhesus Macaque Cohort 1

For inter-species comparison, we used qRT-PCR gene expression (GE) data [low-density array (LDA)] of the eight genes, which were examined on 122 specimens (males = 62; females = 60) of our previously investigated first cohort (herein referred to as cohort 1). After 7 Gy TBI, about two-thirds survived (LD66/60, Table 1). Rhesus macaques were either non-treated or treated with interleukin 12, granulocytecolony stimulating factor, or a combination of both (Table 1). Further information regarding the cohort and the used material and methods are described elsewhere (18).

TABLE 1

Overview Regarding Rhesus Macaques Used in Two Different Cohorts

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Rhesus Macaque Cohort 2

Animals

For second cohort, a total of 64 healthy rhesus macaques (Macaca mulatta, Chinese substrain) were obtained from the National Institutes of Health Animal Center (NIHAC, Poolesville, MD, Table 1). The animals were quarantined for 6–7 weeks prior to the beginning of the experiment. One rhesus macaque was excluded from the study due to a viral infection, leaving 63 rhesus macaques eligible for this study. Overall, there were 40 male and 23 female clinically healthy animals weighing 3.6–8.4 kg and were housed at AFRRI (Bethesda, MA). All animals were kept in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International. Housing requirements, sensory and dietary enrichment details have been described in detail previously (24, 25). Single housing was utilized for the animals for this study and justification for single housing is described earlier (26). All of the procedures performed in this study were in accordance with the animal use protocols approved by the Institutional Animal Care and Use Committee (IACUC, AFRRI) and Department of Defense second-tier approval from the Animal Care and Use Review Office (ACURO). The study was reported in accordance with ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines and with the recommendations made in the Guide for the Care and Use of Laboratory Animals (27).

Drug Preparation and Administration

Gamma-tocotrienol was procured from (American River Nutrition, Hadley, MA, ExcelVite Sdn. Bhd., Perak, Malaysia) and its preparation and administration have been described earlier (28). The dose used in this study range from 37.5 mg/kg and the volume administered to each animal was based on individual animal body weight. At least 24–48 h prior to drug administration, the injection site (dorsal scapular area) was shaved and cleaned to monitor for any skin irritations or abscess formation. GT3 and or vehicle administrations were performed with a sterile 21–25 gauge needle length of 3/4–1″.

Blood Sample Collection

Either seven or one day before irradiation blood was collected by venipuncture from either the saphenous or cephalic vein of the lower leg or from the brachial vein from the upper extremity of the arm. From rhesus macaques of cohort 2, 1 ml of peripheral blood was drawn into PAXgene Blood RNA tubes (PreAnalytiX, a Qiagen/Becton, Dickinson and Company, Franklin Lakes, NJ). After collection, the blood was mixed immediately by inverting the tube 10 times. The tubes were maintained at room temperature in the laboratory overnight and were later stored at –80°C until analysis (29).

Irradiation

Prior to the irradiation procedure, does rate measurements were performed and its description have been provided earlier (30, 31). The animals were fasted for at least 12–18 h prior to irradiation, and at approximately 30-45 min before exposure, all animals received appropriate anesthetics. All details for irradiation procedure have been discussed previously (32). Three different dose groups of 5.8 Gy (n = 31, males n = 20 and females n = 11), 6.5 Gy (n = 16, males n = 4 and females n = 12), and 7.2 Gy (n = 16 males) were used for this study. After the procedure, once the animals were certified to be in stable condition, they were transported back to the housing cages where they completed their recovery.

Euthanasia

After irradiation, animals are prone to exhibit clinical ARS-related signs and symptoms, and daily observations were increased to three times a day (no more than 10 h apart) during the critical period (days 10–20 postirradiation) to assess for moribundity. If an animal reached a state of moribundity, the animal was euthanized. As a surrogate for mortality, moribundity was used for assessment of animals, and were euthanized to minimize pain and distress. All euthanasia criteria and additional details have been provided previously (33). In general, euthanized NHPs were defined as non-survivors (n = 22), while all living animals on day 60 were euthanized and considered as survivors (n = 41).

RNA-Extraction and Quality-Control

Filled PAXgene® Blood RNA tubes were manually thawed, centrifuged, the supernatant discarded, and pellets resuspended with proteinase K augmented buffers. RNA from PAXgene Blood RNA tubes was isolated semi-automatically following the QIAsymphony® Blood RNA Kit (QIAGEN, Hilden, Germany) using the QIAsymphony SP (QIAGEN, Hilden, Germany). This procedure uses the RNA-binding silica surface of magnetic beads. After several washing and digestion steps with DNase I and proteinase K, RNA was isolated automatically, eluted in 80 µl BR5 buffer, heated to 65C for five min, and stored at –20°C.

For quantification, RNA-eluates were measured spectrophotometrically (NanoDrop, PeqLab Biotechnology, Erlangen, Germany). DNA contamination was precluded via PCR using primers for the β-actin gene. qRT-PCR was performed on all specimens with a ratio of A260/A280 nm ≥ 2.0. The quality was addressed by automated electrophoretic integrity measurements (4200 TapeStation System, Agilent Technologies, Santa Clara, CA), and RIN (RNA integrity number) values were calculated. Questionable measurements were confirmed via 18S rRNA-qRT-PCR. Only samples meeting predefined quality criteria [e.g., 18S rRNA-raw cycle-threshold (Ct) values (0.01 ng/reaction) ranging between 20 and 23 are expected for successful qRT-PCR] were further processed, leading to the qRT-PCR.

Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (qRT-PCR)

Aliquots of total RNA (0.6–1 µg) were reverse transcribed with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Life Technologies, Darmstadt, Germany). Equal amounts of template cDNA (10 ng) were used per reaction, mixed with the TaqMan® Universal PCR Master Mix, and gene-specific TaqMan Assays were added ( Supplementary Table S1 (10.1667 RADE-23-0009.1.S1 .xlsx),  https://doi.org/10.1667/RADE-23-00093.1.S1).2 We used standard human TaqMan Assays from our former study (18). Corresponding rhesus monkey TaqMan Assays (Rm) were generated using the same exon regions. The qRT-PCR was performed on a 96-well format using the QuantStudio 12K OA Real-Time PCR System (Thermo Fisher SCINTIFIC Inc., Waltham, MA). The raw cycle threshold was normalized to the diluted 18S rRNA. After normalization, fold change (FC) differences in gene expression between survivors and non-survivors were calculated by the ΔΔ Ct-approach (FC = 2–ΔΔCT) relative to non-survivors used as the calibrator. The fold change refers to several-fold of over or under expression relative to the calibrator. Genes were assumed to be differentially expressed if 0.5 ≥ FC ≥ 2 (16, 34).

Statistical Analysis

Results were presented as normalized Ct values. Intra- and inter-cohort comparisons were performed by the parametrical t-test or the non-parametrical Mann-Whitney Rank Sum test, where applicable. Differences in variance distribution among compared groups were calculated using the Brown-Forsythe test. The chi-square statistics or Fischer's exact test investigated differences in the frequency distribution. Logistic regression and receiver operating characteristics with an odds ratio (OR) and 95% confidence interval (CI), as well as positive and negative predictive values, were calculated for the prediction of survival and non-survival, respectively. P values < 0.05 were defined as significant, and P values ≥ 0.05 but < 0.1 as borderline significant. For inter-cohort comparisons of chromodomain helicase DNA-binding protein 5 (CHD5) normalized Ct values generated by low-density array technology in cohort 1 and 96-well format qRT-PCR in cohort 2, Ct values had to be aligned by subtracting 2.96 Ct values from cohort 1 (median Ct-difference of cohort 2 male non-survivors [12.45] from cohort 1 male non-survivors [15.41]). For statistical analyses and graphical presentations, SAS (release 9.4, Cary NC) and Excel (Microsoft, Redmond, CA), as well as SPW (SigmaPlot, Version 14.5, Jandel Scientific, Erkrath, Germany) and PowerPoint (Microsoft) were used.

RESULTS

Significant and borderline significant differences in RNA copy numbers (normalized Ct values) between survivors and non-survivors could only be validated for CHI3L1 and CHD5 when merging all rhesus macaques regardless of the treatment and the radiation dose of cohort 2 [Table 2 (under cohort 2);  Supplementary Table S2 (10.1667 RADE-23-00099.1.S2.xlsx)https://doi.org/10.1667/RADE-23-00099.1.S2) and  Supplementary Table S3 (10.1667 RADE-23-00099.1.S3.xlsx)https://doi.org/10.1667/RADE-23-00099.1.S3); Figs. 1 and 2;  Supplementary Fig. S1 (10.1667 RADE-23-00099.1.S4.pdf);  https://doi.org/10.1667/RADE-23-00099.1.S4] while ignoring sex differences. Examinations considering treatment and radiation exposure revealed significant/borderline significant differences in certain strata, but an expected pattern, e.g., a dose, treatment, or sex dependency, was not shown (Table 2, lower part; Figs. 1 and 2). In males, deregulation of CHD5 GE in the same direction could be demonstrated in both cohorts, both doses, as well as both species-specific gene detection assays, but these differences did not reach significance and were less consistent in females (Fig. 2). Throughout, non-survivors revealed a tendency of larger variance in CHD5 GE compared to the survivors, caused by a subpopulation of rhesus macaques comprising CHD5 GE values ≤ 11 (Fig. 2). Fold change in CHD5 GE appeared down-regulated in both sexes and reached significance when merged or examined in females only (Table 3). CHI3L1 was down-regulated in females (contrary to cohort 1; Fig. 1) and up-regulated in males, leading to insignificant fold change when merged. Univariate (CHI3L1 and CHD5 GE examined separately) logistic regression analysis corresponded to fold change differences, and bivariate logistic regression analysis (CHI3L1 and CHD5 GE combined) revealed significant/borderline significant contributions of CHD5 GE, which strengthens with increased sample size in models merging both sexes (Table 3, lower part). Lower P values could be calculated using the rhesus macaque gene detection assays compared to the human assay, and in 48 comparisons, fold changes were comparable among both assays (data not shown). Examination of CHD5 GE separately for sex, cohort, and both combined strengthened the association with increasing sample size regarding fold change and odds ratio [n = 104, OR 1.38, 95% CI 1.07–1.79, P = 0.01 (Table 4, lower part)]. However, this association was driven by males [OR 1.62, 95% CI 1.10–2.38, P = 0.01 (Table 4 upper part)] and less by females because fold change in the control range were observed in cohort 1 and down-regulated in cohort 2 (Table 4, middle part). When removing CHD5 Ct values ≤ 11 from these models, all associations became insignificant (Table 4). The ROC curve also shows this for males (Fig. 3). Furthermore, CHD5 Ct values ≤ 11 were mainly associated with increased frequencies (61–100%) of non-survivors, a trend which depending on the sample numbers comprising CHD5 Ct values ≤ 11 reached significances (P = 0.03) in males and accordingly in females (Table 5).

TABLE 2

Presented are the P values from Statistical Comparisons of Gene Expression-Difference Between Survivor and Non-Survivor for the Eight Investigated Genes for Cohort 1 and 2 (Parametrical t-test- or Non-Parametrical Man-Whitney Rank-Sum Test where Applicable)

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TABLE 3

The Genes CHI3L1 and CHD5 Showed Significant Association with Survival in Both Cohorts

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TABLE 4

A Further Characterization of CHD5 Gene Expression (GE) Data Only is Shown Here. For this Comparison, CHD5 GE Values Measured with Different qrt-PCR Techniques in Both Cohorts had to be Aligned (for Details, See Material and Method Part)

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TABLE 5

A Frequency Distribution Comparison among CHD5 Subgroups (CHD5 ≤ 11, Tables Left Side and CHD5 ≥ 14.4, Tables Right Side) is Shown for the Number of Survivors and Non-Survivors using All Samples from Both Cohorts and Separately for Each Cohort and Sex

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FIG. 1

Normalized CHI3L1 gene expression (GE) Ct values are shown for survivors and non-survivors of two irradiated rhesus macaque cohorts, separated for males (upper graph) and females (lower graph). Two different TaqMan assays (human and rhesus) for gene identification were employed. Comparisons on gene expression were performed for different exposures indicated as absorbed doses in Gy. Finally, all measurements for cohort 2 were merged and depicted in the right part of the graphs. Asterisks represent the statistically significant comparison of mean or median CHI3L1 GE values between survivors and non-survivors with *0.1 > P ≥ 0.05, **0.05 > P ≥ 0.01, and ***P < 0.01. Details regarding P values are shown in Table 2.

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FIG. 2

Normalized CHD5 gene expression (GE) Ct values are shown for survivors and non-survivors of two irradiated rhesus macaque cohorts, separated for males (upper graph) and females (lower graph). Two different TaqMan assays (human and rhesus) for gene identification were employed. Comparisons on gene expression were performed for different exposures indicated as absorbed doses in Gy. Finally, all measurements for cohort 2 were merged and depicted in the right part of the graphs. Asterisks represent the statistically significant comparison of mean or median CHD5 GE values between survivors and non-survivors with *0.1 > P ≥ 0.05, **0.05 > P ≥ 0.01, and ***P < 0.01.

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FIG. 3

CHD5 gene expression (GE) measurements of both cohorts were merged, and ROC curves for males were generated. Two ROC curves reflect data generated on all males (bold black line) (ROC = 0.66), males excluding CHD5 > 11 and CHD5 ≥ 14.4 (medium dashed line) (ROC = 0.52), gray boxes mark the area of positive (PPV, lower right) and negative predictive values (NPV, upper right), thus predicting survival and non-survival with 85–100%, respectively.

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A second male subcohort comprising high CHD5 Ct values ≥ 14.4 in both cohorts (n = 5) appeared associated with survival (Table 5, right side), and removing these high CHD5 Ct values converted the model borderline significant (P = 0.051; Table 4). Based on the male probability function of the ROC curves, 8 (12.3%) and 5 (7.7%) from 65 pre-exposure RNA measurements in males, death and survival could be predicted with a negative and positive predictive value ranging between 85–100%. An associated odds ratio reflecting a 62% elevated risk for dying or surviving per unit change (Ct value) in gene expression considers the before mentioned CHD5 thresholds in RNA copy numbers (Fig. 3).

DISCUSSION

Identifying radiosensitive individuals prior to exposure (e.g., a mission to the moon, ARTEMIS) can be beneficial to more accurately assess the individual health risk of individuals exposed to ionizing radiation medically, occupationally or accidentally. Previous work of our group on 122 rhesus macaques identified eight genes significantly associated with survival after radiation exposure, where 66% of the animals survived (18). Accessing a second irradiated rhesus macaques cohort comprising 40 male and 23 female animals provided an opportunity for independent validation of these radiosensitivity-predicting genes. Two out of eight genes, namely CHI3L1 and CHD5, appeared significantly associated with survival, as well. Although radiation-induced gene expression changes for CHI3L1 showed opposing directions in both cohorts, leaving only CHD5 eligible for further analysis. Expected associations with dose or treatment were not found. Instead, another pattern evolved: The association of CHD5 GE with non-survival strengthened with increasing sample size and examining both cohorts and sexes combined. The association was driven by a subpopulation of males comprising CHD5 Ct values ≤ 11. With increasing sample size, the total number (n = 17) of animals comprising CHD5 Ct values ≤ 11 increased as well (Table 2). These values were mostly associated with increased frequencies (61–100%) of male non-survivors (Table 5). When removed this subgroup from logistic regression models, all significant associations of CHD5 with survival became insignificant (Tables 2 and 4; Fig. 3). Another subpopulation (n = 5) of male rhesus macaques comprising CHD5 Ct values ≥ 14.4 was only found in survivors and both cohorts (Table 5). Removing these values reverted the association to borderline significant. Based on these findings, predictions regarding radiosensitivity (dying)/radioresistance (surviving) can be provided for about 20% of males in both cohorts with an 85–100% likelihood (Fig. 3).

Predicting opposing biological effects (dying or surviving) depending on increased or decreased CHD5 copy numbers in male pre-exposure peripheral blood samples might reflect a causal relationship. The results confirm that the pre-exposure transcription status of the cells at the time of radiation exposure would impact survival or non-survival. Nevertheless, no correlation between radiosensitivity and dose or treatment was found by investigating the rhesus macaque cohort. This can be explained by the simple fact that rhesus macaques were not selected into treatment or radiation exposure groups based on their pre-exposure CHD5 RNA copy numbers. Consequently, the missing association appears more by chance, strengthen when examining all animals including the identified CHD5 subcohort (see Table 4 and Fig. 3) and is therefore in line with our hypothesis.

The validated gene CHD5 belongs to the class II, chromodomain helicase DNA (CHD)-binding proteins (35). They are important influencers of radiation-induced signaling transduction pathways and the oxidative stress-induced DNA-double strand break repair (36, 37). Several studies indicate an impact of CHD5 on the G1/S-phase gatekeeper p53 by transcriptional regulations via the cyclin-dependent kinase inhibitor 2A locus (CDK2A) (3840) (Fig. 4). Against this background, CHD5 plays a role in a wide range of tumor entities in various ways (4045). High expression of CHD5 leads to cell cycle arrest and apoptosis in chronic myeloid leukemia patients (41) and inhibits renal cancer cells from further growth and invasion (40). In this light, already high pre-exposure CHD5 copy numbers could predispose the whole organism to death via additional radiation-induced up-regulation of CHD5. This finally leads to the p53 activation and consecutive cell-cycle arrest and apoptosis. To prove this hypothesis, further validation experiments are needed.

FIG. 4

Panel A: Shows the normal cell cycle progression with low CHD5 copy-numbers and homeostasis of factors promoting cell cycle progression as well as arrest, resulting in a controlled G1-S phase transition. CDKN2A-locus is condensed and, this way, not readable for transcription factors. Homolog mouse double minute 2 protein (HMdm2) with its E3-ubiquitin-protein ligase activity can ubiquitinate p53 for degradation, which cannot slow cell cycle progression down (38, 46). Panel B: Shows the process in the presence of high copy numbers of CHD5 as a component of Nucleosome remodeling and deacetylase complex (NuRD). The NuRD with CHD5 specifically interacts with CDKN2A-locus in terms of DNA damage due to several Noxa (38, 39) as well as ionizing radiation. In this way, transcription factors can access CDKN2A-locus, and P16INK4A, as well as P14ARF are produced. This inhibits cyclin-dependent kinase 4 (CDK4) and sequesters HMdm2, respectively. HMdm2 cannot ligate Ubiquitin (Q) to p53, which is activated by phosphorylation (P). In combination, this leads to a blocked G1-S phase transition, resulting in cell cycle arrest and apoptosis (46).

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A gene detection assay based on the human genome was employed in rhesus macaque in the previous cohort 1 study to translate rhesus macaque results into the human species better. For the second validation cohort, complimentary gene detection assays using the rhesus macaque transcriptome were generated. Both species-specific assays showed similar results with slightly improved P values when using the rhesus macaque assays. This confirms the performed measurements and reflects the known homology of both genomes.

Our study bears limitations leaving space for alternative interpretations of our data. Although two rhesus macaque cohorts with a total of 185 animals were examined (including 104 rhesus macaques with CHD5 measurements), only 17 animals showed CHD5 Ct values ≤ 11, and 8 animals showed CHD5 Ct values ≥ 14.4. These groups were identified as the “driver” of statistically significant results in all data combined, as well as male models (n = 8 and n = 5 for both CHD5 threshold values mentioned above). However, in females and cohort 2, half of rhesus macaques with CHD5 Ct values ≤ 11 were associated with non-survivors and the other half with survivors (Table 5). In contrast, male CHD5 Ct values ≤ 11 showed higher frequencies in non-survivors than survivors. For CHD5 Ct values ≥ 14.4, all male rhesus macaques were survivors in both cohorts, unlike the females in cohort 1, where all were non-survivors (Table 5). Hence, a gender effect or a significant association by chance must be considered. Interestingly, after deleting 11≥ CHD5 ≥ 14.4 in females, a significant association (P = 0.04) of remaining CHD5 measurements could be found, and the ROC area increased to 0.76 (data not shown). This might indicate a gender-dependent effect with other CHD5 thresholds for females. Further studies in this regard are required. Our Gnostic analysis approach to predict pre-exposure radiosensitivity is limited because we are focusing on a set of genes. Therefore, we do not depict a complementary bioinformatics approach, where networks of genes are considered. This restriction could explain the identified result differences between both cohorts. In theory, even one gene which was not considered in our previous analysis in cohort one, but altered in cohort two, could modify the network and affect the expected radiosensitivity outcome prediction of our examined candidate genes.

In summary, using two species specific gene detection assays, similar gene expression results were generated, thus, confirming the performed measurements. Only one (CHD5) from eight genes could be independently validated in two rhesus macaque cohorts. Significant associations of CHD5 GE with survival were driven by male subgroups comprising either higher CHD5 copy numbers in pre-exposure peripheral blood samples with a predisposition to die or lowered CHD5 copy numbers with a predisposition to survive after irradiation. Predictions regarding radiosensitivity (dying) and radioresistance (surviving) could be provided for about 20% of males in both cohorts. These findings might reflect a causal relationship and require further research.

SUPPLEMENTARY MATERIALS

 Supplementary Table S1 (10.1667 RADE-23-0009.1.S1 .xlsx). The table provides an overview of raw and normalized Ct values of eight genes examined with a human (Hs) and a rhesus (Rm) specific TaqMan gene expression detection assay for each of the 63 examined rhesus macaque of cohort 2. Further characteristics such as sex (males, m; females f), H-ARS severity category, treatment regimen (gamma-tocotrienol, GT3), and dose are provided.

 Supplementary Table S2 (10.1667 RADE-23-00099.1.S2.xlsx). The table provides an overview of sample numbers of survivors and non-survivors, mean normalized Ct values, and corresponding fold-change calculations of eight genes examined with a human-specific TaqMan gene expression detection assay in two cohorts and separately for males (m) and females (f). Significant P values are presented in bold.

 Supplementary Table S3 (10.1667 RADE-23-00099.1.S3.xlsx). The table provides an overview of the human and corresponding rhesus macaques TaqMan Assays used within this study.

 Supplementary Fig. S1 (10.1667 RADE-23-00099.1.S4.pdf). Normalized gene expression (GE) Ct values from six genes, namely DYSF, EPX, IGF2BP1, LCN2, MBOAT4, and SLC22A4 (referred to as Fig. 1AF), are shown for survivors and non-survivors of two irradiated rhesus macaque cohorts and separately for males (upper graph) and females (lower graph). Two different TaqMan assays (human and rhesus assays) for gene identification were employed. Comparisons on gene expression were performed for different exposures indicated as absorbed doses in Gy. Finally, all measurements for cohort 2 were merged and depicted in the right part of the graphs.

ACKNOWLEDGMENTS

We thank Sven Doucha-Senf and Oliver Wittmann for their technical support. The authors gratefully acknowledge the research support from the Congressionally Directed Medical Research Programs (W81XWH-15-C-0117, JW140032) of the U.S. Department of Defense to VKS. The opinions or assertions contained herein are the private views of the authors and are not necessarily those of the Uniformed Services University of the Health Sciences or the Department of Defense.

©2024 by Radiation Research Society. All rights of reproduction in any form reserved.

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Notes

[1] 2 Editor's note. The online version of this article (DOI:  https://doi.org/10.1667/RADE-23-00099.1.S1) contains supplementary information that is available to all authorized users.

D. Schwanke, O. O. Fatanmi, S. Y. Wise, P. Ostheim, S. Schüle, G. Kaletka, S. Stewart, T. Wiegel, V. K. Singh, M. Port, and M. Abend "Validating Radiosensitivity with Pre-Exposure Differential Gene Expression in Peripheral Blood Predicting Survival and Non-Survival in a Second Irradiated Rhesus Macaque Cohort," Radiation Research 201(5), 384-395, (8 May 2024). https://doi.org/10.1667/RADE-23-00099.1
Received: 2 June 2023; Accepted: 27 September 2023; Published: 8 May 2024
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