Open Access
How to translate text using browser tools
8 May 2024 Validating a Four-gene Set for H-ARS Severity Prediction in Peripheral Blood Samples of Irradiated Rhesus Macaques
D. Schwanke, S. Schüle, S. Stewart, O. O. Fatanmi, S. Y. Wise, C. Hackenbroch, T. Wiegel, V. K. Singh, M. Port, M. Abend, P. Ostheim
Author Affiliations +
Abstract

Increased radiological and nuclear threats require preparedness. Our earlier work identified a set of four genes (DDB2, FDXR, POU2AF1 and WNT3), which predicts severity of the hematological acute radiation syndrome (H-ARS) within the first three days postirradiation In this study of 41 Rhesus macaques (Macaca mulatta, 27 males, 14 females) irradiated with 5.8–7.2 Gy (LD29-50/60), including some treated with gamma-tocotrienol (GT3, a radiation countermeasure) we independently validated these genes as predictors in both sexes and examined them after three days. At the Armed Forces Radiobiology Research Institute/Uniformed Services University of the Health Sciences, peripheral whole blood (1 ml) of Rhesus macaques was collected into PAXgene® Blood RNA tubes pre-irradiation after 1, 2, 3, 35 and 60 days postirradiation, stored at –80°C for internal experimental analyses. Leftover tubes from these already ongoing studies were kindly provided to Bundeswehr Institute of Radiobiology. RNA was isolated (QIAsymphony), converted into cDNA, and for further gene expression (GE) studies quantitative RT-PCR was performed. Differential gene expression (DGE) was measured relative to the pre-irradiation Rhesus macaques samples. Within the first three days postirradiation, we found similar results to human data: 1. FDXR and DDB2 were up-regulated, FDXR up to 3.5-fold, and DDB2 up to 13.5-fold in the median; 2. POU2AF1 appeared down regulated around tenfold in nearly all Rhesus macaques; 3. Contrary to human data, DDB2 was more up-regulated than FDXR, and the difference of the fold change (FC) ranged between 2.4 and 10, while the median fold changes of WNT3, except days 1 and 35, were close to 1. Nevertheless, 46% of the Rhesus macaques showed down-regulated WNT3 on day one postirradiation, which decreased to 12.2% on day 3 postirradiation. Considering the extended phase, there was a trend towards decreased fold changes at day 35, with median-fold changes ranging from 0.7 for DDB2 to 0.1 for POU2AF1, and on day 60 postirradiation, DGE in surviving animals was close to pre-exposure values for all four genes. In conclusion, the diagnostic significance for radiation-induced H-ARS severity prediction of FDXR, DDB2, and POU2AF1 was confirmed in this Rhesus macaques model. However, DDB2 showed higher GE values than FDXR. As shown in previous studies, the diagnostic significance of WNT3 could not be reproduced in Rhesus macaques; this could be due to the choice of animal model and methodological challenges.

INTRODUCTION

Radiological and nuclear incidents can occur as a result of civil accidents, meltdowns in nuclear power plants, or intentionally via a terroristic attack using a radiological or nuclear dispersion device or the military use of atomic bombs (1, 2). In any case, they have the potential to produce mass casualties and even more so-called “worried well” (individuals who did not get harmed or exposed to radioactive contamination or radiation) (3). From the human experience with improperly disposed radioactive sources, as in Goiania (4), the nuclear meltdown in Chernobyl (5), and the use of atomic bombs as in the Castle Bravo test series (6), it is likely that thousands of potentially exposed or irradiated individuals must be triaged. The scientific community has been called upon to develop tools for triaging patients that might need immediate and specific medical care and surveillance with regard to development of the acute radiation syndrome (ARS) (7). It is not only the absorbed dose that allows prediction of the subsequent course of a patient's ARS; instead, factors such as fractionation, radiation quality, dose rate, external or internal contamination, as well as how the individual was exposed [homogenous vs. inhomogeneous, or total-body (TBI) vs. partial-body (PBI) irradiation], and individual radiosensitivity also play major roles (8).

An early and high-throughput clinical outcome tool for predicting ARS severity would be useful for triage during a large-scale radiological and nuclear event. Gene expression (GE) analysis is a promising approach toward handling the requirements of such a tool (920). In previous work, we showed that a set of four genes comprising FDXR, DDB2, POU2AF1, and WNT3 was able to distinguish non-exposed as well as patients that later developed mild and severe hematologic acute radiation syndrome (H-ARS) (1013, 21, 22). This corresponds to the METREPOL-Algorithm¨s H-ARS categories (23). Our group previously showed that it is of benefit, when looking at the medical treatment consequences, to break the H-ARS categories down into three groups: H0 represents the unexposed, H0-1 represents those with no or low radiation exposure with sub-clinical H-ARS for which no hospitalization is needed, and H2-4 represents those with mild to severe H-ARS, when early treatment and hospitalization is required (21). In this study, the ionizing radiation dose was comparably high (5.8–7.2 Gy) with a LD29-50/60 and might be expected to result in a mild to fatal H2-4 H-ARS severity. The H-ARS severity predicting gene set has already been validated in vitro (12), in vivo in baboons (10, 11, 21), and in irradiated oncology patients (10, 22). The Rhesus macaques genome shares 93% of its sequence with the human genome (24). In cooperation with Armed Forces Radiobiology Research Institute (AFRRI), we independently validated and further investigated this model in a new species, Macaca mulatta, which is even closer related to humans than baboons. Additionally, due to the design of our study, we could evaluate the GE changes of our four gene-set over a 60-day time course postirradiation, exceeding the 3-day postirradiation measurements of previous studies.

MATERIALS AND METHODS

Animals

Sixty-four healthy Rhesus macaques (Macaca mulatta, Chinese sub-strain) 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 macaques was excluded from the study due to a virus infection, leaving 63 Rhesus macaques eligible for this study. Males and females were assigned to dose groups but a lower number of females were assigned due to availability of Rhesus macaques s at a particular time and a lower number of females available to us. Overall, 40 male and 23 female clinically healthy individuals weighing 3.6–8.4 kg were housed at the Armed Forces Radiobiology Research Institute (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 previously described in elsewhere (25, 26). Single housing was utilized for the animals for this study, and the justification for single housing was described earlier (27). 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 (28).

TABLE 1

Distribution of the Animals over the Different Strata, Sorted by Dose, Sex, Treatment Group, and Survival Status

img-A0JHS_504.gif

Drug Preparation and Administration

Gamma-tocotrienol (GT-3) was procured from American River Nutrition (Hadley, MA/ExcelVite Sdn. Bhd., Perak, Malaysia) and its preparation and administration have been described earlier (29). The dose used in this study ranged from 37.5–75 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 0.75–1 inch.

Blood Sample Collection

Blood was collected by venipuncture from either the saphenous vein on the caudal aspect of the lower leg or the brachial vein from the upper extremity of the arm. One ml of peripheral blood was drawn into PAXgene Blood RNA tubes (PreAnalytiX, a Qiagen/Becton, Dickinson, and Company, Franklin Lakes, NJ) on either day 1 or 7 prior to irradiation and on days 1, 2, 3, 35 and 60 postirradiation. 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 further analysis (30).

Irradiation

Prior to irradiation, dose rate measurements were performed as described earlier (31, 32). The animals were fasted for at least 12–18 h prior to irradiation, at approximately 30–45 min before exposure, all animals received appropriate anesthetics. Irradiations were performed using a cobalt-60 source with a dose rate of 0.6 Gy/min. NHPs were fastened in a sitting position within the central beam to perform a total-body irradiation (TBI). All details for the irradiation procedure have been discussed previously (33). 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 were prone to exhibiting 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. Moribundity was used as a surrogate for the mortality assessment of animals, and they were euthanized to minimize pain and distress. All euthanasia criteria and additional details have been provided previously (34). In general, euthanized Rhesus macaques were defined as non-survivors (n = 22), while all surviving animals on day 60 were euthanized and considered survivors (n = 41).

Sample Selection

Expecting a high H-ARS severity degree, whether the radiation dose was 5.8, 6.5 or 7.2 Gy, sample sets from 41 Rhesus macaques were analyzed to keep the remaining samples for other tasks. Also, data appeared very homogenous, so the study could be restricted to the aforementioned number of Rhesus macaques. All sample sets of the animals exposed to 5.8 Gy were processed for this study. From those animals exposed to 6.5 Gy and 7.2 Gy, five sample sets each were randomly picked the way that the survivor to non-survivor ratio equals the one of the whole cohort (Table 1). In total, 219 blood specimens were investigated.

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 using the QIAsymphony® Blood RNA Kit (QIAGEN, Hilden, Germany) and the QIAsymphony® SP Kit (QIAGEN). The 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 65°C 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/A280nm ≥ 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 pre-defined quality criteria [e.g., 18S rRNA-raw Ct values (0.01 ng/reaction) ranging between 20 and 28 are expected to be 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 (0.5 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 for FDXR (Hs01031617_m1), DDB2 (Hs00172068_m1), POU2AF1 (Hs01573371_m1), and WNT3 (Hs00902257_m1) were added. The genes were measured in duplicate, and 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 (Ct) was normalized to the diluted 18S rRNA (Hs99999901_g1). After normalization, fold change (FC) differences in gene expression were calculated by the –ΔΔCt-approach relative to unexposed samples of the same Rhesus macaques used as the calibrator FC = fi01_504.gif. The fold change refers to several fold of over- or underexpression relative to the calibrator. Genes were assumed to be differentially expressed if 0.5 ≥ FC ≥ 2 (10, 35).

Statistical Analysis

Results were presented as normalized Ct values, mean fold change and median fold change. Differences in GE were investigated by comparing each day after exposure with the pre-exposure set to the reference. We also checked for varieties in the strata sex, survival, exposure dose, and treatment. Probability distribution was addressed by Shapiro-Wilks and the comparison of variance by Brown Forsythe test. Due to non-normally distributed results, GE changes over time for all individuals were investigated by a Kruskal-Wallis one way analysis of variance (ANOVA) on ranks. For the analysis of the mentioned strata, two-way ANOVAs (Holm-Sidak method) were performed to compare the pre-exposure animals' GE to a reference group (either female, survivor or untreated animals), and the subsequent interactions over time were analyzed. For the stratum exposure dose, the ANOVA was done pairwise stratified by time and exposure dose. P values < 0.05 were defined as significant. Also, univariate linear regression analysis was performed, searching for dose-response relationships in GE. 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

Time Course of Gene Expression Changes

In all animals, for FDXR we saw an increase in gene expression after irradiation relative to unexposed animals, with the increase becoming significant on day 2 postirradiation (P < 0.001, Fig. 1 and Table 2) with a median fold change of 2.3. GE of FDXR increased until day 3 up to a median fold change of 3.5 (P < 0.001). On day 35, we saw decreased GE (median FC = 0.5) but this change was not significantly different. On day 60, GE was nearly that of unexposed animals. Expression of DDB2 was already significantly increased one day postirradiation (median FC = 3.4, P < 0.001) and increased further after 2 days (median FC = 5.4), up to a median fold change of 13.5 through day 3 (Fig. 1 and Table 2). On days 35 and 60, GE for DBB2 was similar to FDXR. GE of POU2AF1 declined after irradiation (Fig. 1). The approximate 8–10 fold decrease in GE reached significance on day 1 postirradiation (P < 0.001, median FC ≈ 0.13 to 0.09, Fig. 1 and Table 2) and remained decreased until day 35. As observed for FDXR and DDB2, the GE of POU2AF1 on day 60 resembled pre-exposure GE values. WNT3 showed a high variance in GE levels 1–3 days and 60 days postirradiation. Only on day 35 postirradiation was a significant downregulation observed.

FIG. 1

Boxplots superimposed over corresponding jitter plots reflecting differential gene expression (DGE) for each animal and the four genes over time. Boxes contain 50% of the data (25–75th percentiles). The error bars represent the 10th and 90th percentiles, while the white circles represent the 5th and 95th percentiles. Some outliers are not shown. The continuous horizontal lines indicate the position of the median, while the interrupted one indicates that of the mean. Significant changes in GE relative to unexposed animals are marked with asterisks (*P < 0.05, **P < 0.01, ***P < 0.001). The gray area in the background adjusts for methodological variance and is defined for fold changes between 0.5 and 2. The interrupted white line refers to FC = 1 as a reference for GE prior to exposure.

img-z4-1_504.jpg

TABLE 2

Descriptive Statistics [n – Number of Evaluable Animals in the Subgroup, Mean and Median Fold Change and Standard Deviation (Std Dev)], as well as P Values for Comparisons to Pre-Exposure Status and in-between Subgroups for each Day and each Gene

img-AhmH_504.gif

If we consider a twofold up- or downregulation to represent a robust change in GE, Table 3 shows that between the first three days postirradiation, 90–100% of the irradiated animals could have been identified as exposed individuals based on GE of DDB2 and POU2AF1. In a receiver-operator-characteristics (ROC) analysis, DDB2 and POU2AF1 showed a high diagnostic strength with an area under the curve ranging from 0.88–0.97 over the first 3 days postirradiation (Fig. 2). A bivariate model of DDB2 and POU2AF1 reached an area under the curve of 1.0 on days 1–3 postirradiation (Fig. 2). While the percentage of correctly identified animals, based on FDXR and DDB2 GE, increased from day 1 to day 3 from 22–78% and from 78–100%, respectively, WNT3 was reduced in its diagnostic strength, decreased from at least 46% on day 1 down to 12% on day 3. Based on POU2AF1 GE, it was possible to identify 90% of the exposed individuals as early as day one; this represented the highest diagnostic reliability of the four genes on day one. POU2AF1 GE remained around tenfold downregulated up to day 35 postirradiation. Between 80 to 89% of the irradiated animals could have been identified as having been exposed over this longer period.

TABLE 3

Frequency Distribution of the Four Genes Grouped by FC ≥ 2, 2 > FC > 0.5, and FC ≤ 0.5 for each Day (Bold Numbers Left Part of the table) after Irradiation

img-AlFc_504.gif

FIG. 2

Sensitivity vs. 1-specificity of a receiver-operator characteristic curve (ROC) for days 1 to 3 after irradiation for DDB2, POU2AF1, and a bivariate model of these two genes. Area under the curve describes the area under the curve for each gene and the bivariate model.

img-z6-4_504.jpg

GE Differences by Sex

For FDXR GE, we saw a tendency toward a higher median fold change for females (e.g., 5.0 vs. 2.2 on day 2, and 6 vs. 3.5 on day 3 postirradiation, Table 2), but there was no significant difference for any time-point after exposure. For POU2AF1 GE, we observed a tendency toward a more pronounced, but insignificant, downregulation after exposure regarding the median fold change for males for each single time point (0.2 vs. 0.1 on days 1 and 3 and 0.3 vs 0.1 on day 2 postirradiation, Table 2). For DDB2 and WNT3 GE, no other sex-specific effects could be observed.

GE Differences by Survival Status

For FDXR, DDB2 POU2AF1, and WNT3, no GE differences between survivors and non-survivors could be found, except for WNT3 on day 2 postirradiation (median FC = 1.3, P = 0.04, Table 2).

GE Differences by Dose

A significant association between GE and dose (P = 0.007, P < 0.001) was observed for FDXR, DDB2 and POU2AF1, but this association was absent for WNT3 and FDXR on day 1 (Fig. 3). These associations became insignificant when omitting the pre-exposure data set (data not shown).

FIG. 3

GE dose-response curves for each gene 1–3 days postirradiation, displayed as the median fold change relative to unexposed animals (black, gray, and white circles) for three dose groups as well as unexposed animals. The upper three graphs contain the GE for DDB2 and FDXR, and the lower ones POU2AF1 and WNT3. Error bars represent the standard error of the mean. r2 specifies the corresponding correlation coefficient. P values refer to the slope of the assumed graph.

img-z7-1_504.jpg

GE Differences by Treatment Status

For all four genes and examined time points, and even for pre-irradiation conditions, treated Rhesus macaques revealed higher GE values than untreated Rhesus macaques (Fig. 4, Table 2). These GE differences were significant in most comparisons, and median fold change of up to 25.5 vs. 10.0 at day 3 for DDB2 was observed (Fig. 3, Table 2).

FIG. 4

Differences in DGE between untreated (white squares) and treated animals (light gray squares) over time postirradiation. Squares contain 50% of the data (25–75th percentiles). The error bars represent the 10th and 90th percentiles, respectively, while the white circles represent the 5th and 95th percentiles. Some outliers are not shown. The continuous horizontal lines indicate the position of the median, while the interrupted one indicates that of the mean. Significant changes in gene expression (GE) between untreated and treated animals are marked with asterisks (*P < 0.05, **P < 0.01, ***P < 0.001). The gray area in the background adjusts for methodological variance and is defined for fold changes between 0.5 and 2. The interrupted white line refers to FC = 1 as a reference for GE prior to exposure.

img-z8-1_504.jpg

DISCUSSION

As the war in Ukraine raises the threat of large-scale radiological and nuclear incidents, it is essential to be prepared. Early and high-throughput diagnostics for triage purposes are required to supply limited resources to those in need of it (7). Early prediction of the clinical outcome, namely the severity of the life-threatening ARS, allows for early treatment decisions to improve prognosis and selection of those individuals requiring limited treatment resources and rare clinical infrastructure such as intensive care units (21).

This study aimed to validate a set of four genes which previously showed promising results for predicting H-ARS in baboon and human in vivo and ex vivo studies (9, 10, 1214, 21, 22, 36). Furthermore, the current study followed irradiated Rhesus macaques up to 60 days postirradiation. Therefore, we showed that GE changes of these genes could be examined over a prolonged period of time exceeding the 3-day period postirradiation in during which these changes were examined in previous baboon and human studies (9, 10, 1214, 21, 22, 36).

Previous studies indicated a mild-to-severe hematological severity degree (24) of the ARS when FDXR and/or DDB2 exceeded a twofold threshold in GE relative to unexposed in combination with other genes (21). This could be shown for DDB2 on the first day, and for FDXR starting on the second day in our current study on Rhesus macaques (Fig. 1, Table 2). GE values increased up to 3 days postirradiation, thus confirming both genes could be used for diagnostic purposes of H-ARS severity prediction in this model as well (Fig. 1, Table 2). However, previous studies gave reasons to expect a peak in GE at 24 h postirradiation, but decreasing GE of FDXR and DDB2 from 24–72 h postirradiation (12, 37). The response of FDXR in Macaca mulatta appears more subdued and delayed compared to numerous human studies. The same is true for DDB2. Interestingly, in our Rhesus macaques study, DDB2 throughout revealed several-fold higher GE changes than FDXR, but in human in vivo and ex vivo models, FDXR appeared stronger deregulated compared to DDB2 (12). However, FDXR in baboons was even down regulated in contrast to e.g., 30-fold upregulation, as cited in many publications using irradiated human blood (12). POU2AF1 showed a pronounced downregulation after 24 h which, in contrast to baboon and human data (10, 12), lasted until day 3 and was found at day 35 as well. This indicates species-specific differences. However, the diagnostic significance of our study remained unaltered, as indicated by the almost complete separation of unexposed (H0) from H2-4 ARS severity groups using logistic regression analysis (Fig. 2). In our Rhesus macaques study, DDB2, combined with POU2AF1, showed a complete separation of both groups (Fig. 2). However, although marginally present, no significant sex-dependent GE changes were found, consistent with other studies (14, 38). Furthermore, survival status and treatment did not affect discrimination between the two groups.

In previous studies, a downregulation of POU2AF1 and/or WNT3 that was ≥ twofold in combination with a ≥twofold upregulation of FDXR and/or DDB2 indicated an H-ARS 2-4 severity degree (21). In our Rhesus macaques study, POU2AF1 responded as known from previous human studies (11), and median GE values appeared tenfold down regulated, independent of sex, survival, or treatment. These pronounced GE changes were found on the first day after irradiation and, if used as a single criterion, would already identify 90% of the Rhesus macaques which later developed an aggravated H-ARS 2-3 severity degree (Table 3). Other than POU2AF1, no pronounced downregulation of WNT3 could be found, as expected due to previous studies conducted on baboons, human in vivo and ex vivo blood studies, as well as CTx-treated breast cancer patients used as a surrogate cohort for rare whole-body irradiated patients (22, 36). Based on WNT3 GE changes on day 1 after irradiation, 46% of the exposed Rhesus macaques would have been correctly categorized as H2-4 H-ARS severity degree (Table 3). However, this effect was inconsistent over time; therefore, the diagnostic value of WNT3 in this animal model could not be reproduced. Detection of radiation-induced lowered WNT3 copy numbers appears challenging, as already noted in human ex vivo studies (12, 21). Baseline raw Ct values close to the upper limit of the qRT-PCR linear dynamic range restrict the identification of down-regulated WNT3 copy numbers since raw Ct values increase with decreasing RNA copy numbers. Therefore, a downregulation cannot be detected in some individuals with high baseline raw Ct values. This could be observed in our Rhesus macaques study as well. To overcome this limitation and to increase the robustness of our H-ARS predictive gene set, we introduced the redundant application of two genes (POU2AF1 and WNT3) based on previous studies. We interpreted the pronounced downregulation of one or both genes as an indication for a later developing H-ARS 2-4 severity degree in consideration with an upregulation of FDXR and/or DDB2 (21). For improved WNT3 detection, we also added five times more cDNA into the qRT-PCR reaction compared to the other genes, but that did not sufficiently adjust for the limitations as described.

Our Rhesus macaques study showed a downregulation of all four genes at day 35 postirradiation, and all GE values at 60 days postirradiation revealed values in the control range (Fig. 1). This effect was independent of sex, survival status, and treatment and might indicate an active response of the irradiated Rhesus macaques since GE values returned to normal values at day 60. The downregulation on day 35 postirradiation might have diagnostic implications, but the underlying mechanism, as well as missing data filling the 4–34 daytime gap after irradiation, requires further investigation. A persistent downregulation of almost all 27 evaluable genes out of 34 genes that were examined in lethally irradiated Rhesus macaques pre-mortem was recently published (39). However, the observed transient downregulation in our Rhesus macaques study might indicate another response. To our knowledge, no further GE examinations of our gene set on day 35 postirradiation are published.

All genes in our study revealed an expected and significant association with dose (Fig. 3). However, this association was driven by unexposed samples because GE values in the dose range of 5.8–7.0 Gy persisted and showed no significant differences for all four genes. A saturation in GE values with doses exceeding 4 Gy has been independently demonstrated in several large-scale inter-laboratory comparison exercises, including up to eight different laboratories (4042). Hence, our observations are in line with cited work.

Although of no impact regarding the diagnostic value of our four-gene set, higher GE values in treated compared with untreated Rhesus macaques were consistently observed for all genes and at each time point before and after irradiation (Fig. 3). An effect related to the administered vehicle (olive oil) or GT3 treatment seems most likely, but the later different treatment groups already differ significantly in GE before irradiation and before neither GT3 nor vehicle was administered. However, for this study, new vendors of GT3 were selected and the specific formulation used in this study was found not to be efficacious (probably caused by unstable emulsion of GT3). Since this effect was present in all genes and observed even pre-exposure and aggravated over time, it probably reflects unknown and uncontrolled aspects inherent to our animal model. Further research in this regard is required and represents another limitation of our study.

In conclusion, the diagnostic significance for radiation-induced H-ARS severity prediction of FDXR, DDB2, and POU2AF1 could be confirmed in this Rhesus macaques model as well, except that DDB2 showed higher GE values than FDXR. The diagnostic significance of WNT3, as demonstrated in previous studies, could not be reproduced in Rhesus macaques, although this may be due to the animal model and methodological challenges.

ACKNOWLEDGMENTS

We are very thankful for the sophisticated technical support provided by Sven Doucha-Senf and Oliver Wittmann. 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 US Department of Defense.

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

REFERENCES

1.

Clarke R, Valentin J, International Commission on Radiological Protection Task G. ICRP Publication 109: Application of the Commission's Recommendations for the protection of people in emergency exposure situations. Ann ICRP 2009; 39(1):1–110.  https://doi.org/10.1016/j.icrp.2009.05.004Google Scholar

2.

Kai M, Homma T, Lochard J, Schneider T, Lecomte JF, Nisbet A, et al. ICRP Publication 146: Radiological protection of people and the environment in the event of a large nuclear accident: Update of ICRP PUBLICATIONS 109 AND 111. Ann ICRP 2020; 49(4): 11–135.  https://doi.org/10.1177/0146645320952659Google Scholar

3.

del Rosario Perez M, Carr Z, Rojas-Palma C, van der Meer K, Smith K, Rahola T, et al. A new handbook on triage, monitoring and treatment of people following malevolent use of radiation. Health Phys 2010; 98(6):898–902.  https://doi.org/10.1097/HP.0b013e3181c4b33fGoogle Scholar

4.

Roberts L. Radiation accident grips Goiania. Science 1987; 238(4830): 1028–31.  https://doi.org/10.1126/science.3685964Google Scholar

5.

United N. Sources and Effects of Ionizing Radiation, United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2008 Report, Volume II: Report to the General Assembly, with Scientific Annexes C, D and E - Effects. New York: United Nations; 2011. Google Scholar

6.

Ford KW. Building the H Bomb: A personal History, Kenneth W. Ford; Singapore; World Scientific; 2015. Google Scholar

7.

Chaudhry MA. Biomarkers for human radiation exposure. J Biomed Sci 2008; 15(5):557–63.  https://doi.org/10.1007/s11373-008-9253-zGoogle Scholar

8.

Port M, Majewski M, Abend M. Radiation dose is of limited clinical usefulness in persons with acute radiation syndrome. Radiat Prot Dosimetry 2019; 186(1):126–9.  https://doi.org/10.1093/rpd/ncz058Google Scholar

9.

Port M, Ostheim P, Majewski M, Voss T, Haupt J, Lamkowski A, et al. Rapid high-throughput diagnostic triage after a mass radiation exposure event using early gene expression changes. Radiat Res 2019; 192(2):208–18.  https://doi.org/10.1667/RR15360.1Google Scholar

10.

Port M, Majewski M, Herodin F, Valente M, Drouet M, Forcheron F, et al. Validating baboon ex vivo and in vivo radiation-related gene expression with corresponding human data. Radiat Res 2018; 189(4): 389–98.  https://doi.org/10.1667/RR14958.1Google Scholar

11.

Port M, Herodin F, Valente M, Drouet M, Lamkowski A, Majewski M, et al. First generation gene expression signature for early prediction of late occurring hematological acute radiation syndrome in baboons. Radiat Res 2016; 186(1):39–54.  https://doi.org/10.1667/RR14318.1Google Scholar

12.

Ostheim P, Coker O, Schule S, Hermann C, Combs SE, Trott KR, et al. Identifying a diagnostic window for the use of gene expression profiling to predict acute radiation syndrome. Radiat Res 2021; 195(1):38–46.  https://doi.org/10.1667/RADE-20-00126.1Google Scholar

13.

Abend M, Blakely WF, Ostheim P, Schuele S, Port M. Early molecular markers for retrospective biodosimetry and prediction of acute health effects. J Radiol Prot 2022; 42(1):843–54.  https://doi.org/10.1088/1361-6498/ac2434Google Scholar

14.

Agbenyegah S, Abend M, Atkinson MJ, Combs SE, Trott KR, Port M, et al. Impact of inter-individual variance in the expression of a radiation-responsive gene panel used for triage. Radiat Res 2018; 190(3):226–35.  https://doi.org/10.1667/RR15013.1Google Scholar

15.

Polozov S, Cruz-Garcia L, Badie C. Rapid gene expression based dose estimation for radiological emergencies. Radiat Prot Dosimetry 2019; 186(1):24–30.  https://doi.org/10.1093/rpd/ncz053Google Scholar

16.

Kabacik S, Mackay A, Tamber N, Manning G, Finnon P, Paillier F, et al. Gene expression following ionising radiation: identification of biomarkers for dose estimation and prediction of individual response. Int J Radiat Biol 2011; 87(2):115–29.  https://doi.org/10.3109/09553002.2010.519424Google Scholar

17.

O'Brien G, Cruz-Garcia L, Majewski M, Grepl J, Abend M, Port M, et al. FDXR is a biomarker of radiation exposure in vivo. Sci Rep 2018; 8(1):684.  https://doi.org/10.1038/s41598-017-19043-wGoogle Scholar

18.

Paul S, Amundson SA. Development of gene expression signatures for practical radiation biodosimetry. Int J Radiat Oncol Biol Phys 2008; 71(4):1236–44.  https://doi.org/10.1016/j.ijrobp.2008.03.043Google Scholar

19.

Ghandhi SA, Smilenov LB, Elliston CD, Chowdhury M, Amundson SA. Radiation dose-rate effects on gene expression for human biodosimetry. BMC Med Genomics 2015; 8:22.  https://doi.org/10.1186/s12920-015-0097-xGoogle Scholar

20.

Paul S, Smilenov LB, Elliston CD, Amundson SA. Radiation dose-rate effects on gene expression in a mouse biodosimetry model. Radiat Res 2015; 184(1):24–32.  https://doi.org/10.1667/RR14044.1Google Scholar

21.

Port M, Herodin F, Drouet M, Valente M, Majewski M, Ostheim P, et al. Gene expression changes in irradiated baboons: A summary and interpretation of a decade of findings. Radiat Res 2021; 195(6):501–21.  https://doi.org/10.1667/RADE-20-00217.1Google Scholar

22.

Schule S, Bristy EA, Muhtadi R, Kaletka G, Stewart S, Ostheim P, et al. Four genes predictive for the severity of hematological damage reveal a similar response after X irradiation and chemotherapy. Radiat Res 2023; 199(2):115–23.  https://doi.org/10.1667/RADE-22-00068.1Google Scholar

23.

Fliedner TM. Medical management of radiation accidents: Manual on the acute radiation syndrome. London, British Institute of Radiology; 2001. Google Scholar

24.

Rhesus Macaque Genome S, Analysis C, Gibbs RA, Rogers J, Katze MG, Bumgarner R, et al. Evolutionary and biomedical insights from the rhesus macaque genome. Science 2007; 316(5822): 222–34.  https://doi.org/10.1126/science.1139247Google Scholar

25.

Phipps AJ, Bergmann JN, Albrecht MT, Singh VK, Homer MJ. Model for evaluating antimicrobial therapy to prevent life-threatening bacterial infections following exposure to a medically significant radiation dose. Antimicrob Agents Chemother 2022; 66(10):e0054622.  https://doi.org/10.1128/aac.00546-22Google Scholar

26.

Garg S, Garg TK, Wise SY, Fatanmi OO, Miousse IR, Savenka AV, et al. Effects of gamma-tocotrienol on intestinal injury in a GI-specific acute radiation syndrome model in nonhuman primate. Int J Mol Sci 2022; 23(9).  https://doi.org/10.3390/ijms23094643Google Scholar

27.

Carpenter AD, Li Y, Janocha BL, Wise SY, Fatanmi OO, Maniar M, et al. Analysis of the proteomic profile in serum of irradiated nonhuman primates treated with Ex-Rad, a radiation medical countermeasure. J Proteome Res 2023; 22:1116–26.  https://doi.org/10.1021/acs.jproteome.2c00458Google Scholar

28.

National Research Council of the National Academy of Sciences. Guide for the care and use of laboratory animals. 8th ed. Washington, DC: National Academies Press; 2011. Google Scholar

29.

Garg S, Garg TK, Miousse IR, Wise SY, Fatanmi OO, Savenka AV, et al. Effects of gamma-tocotrienol on partial-body irradiation-induced intestinal injury in a nonhuman primate model. Antioxidants 2022; 11(10):1895.  https://doi.org/10.3390/antiox11101895Google Scholar

30.

Li Y, Singh J, Varghese R, Zhang Y, Fatanmi OO, Cheema AK, et al. Transcriptome of rhesus macaque (Macaca mulatta) exposed to total-body irradiation. Sci Rep 2021; 11(1):6295.  https://doi.org/10.1038/s41598-021-85669-6Google Scholar

31.

Pannkuk EL, Laiakis EC, Garcia M, Fornace AJ, Jr., Singh VK. Nonhuman primates with acute radiation syndrome: Results from a global serum metabolomics study after 7.2 Gy total-body irradiation. Radiat Res 2018; 190(5):576–83.  https://doi.org/10.1667/RR15167.1Google Scholar

32.

Pannkuk EL, Laiakis EC, Fornace AJ, Jr., Fatanmi OO, Singh VK. A metabolomic serum signature from nonhuman primates treated with a radiation countermeasure, gamma-tocotrienol, and exposed to ionizing radiation. Health Phys 2018; 115(1):3–11.  https://doi.org/10.1097/HP.0000000000000776Google Scholar

33.

Vellichirammal NN, Sethi S, Pandey S, Singh J, Wise SY, Carpenter AD, et al. Lung transcriptome of nonhuman primates exposed to total- and partial-body irradiation. Mol Ther Nucleic Acids 2022; 29: 584–98.  https://doi.org/10.1016/j.omtn.2022.08.006Google Scholar

34.

Singh VK, Fatanmi OO, Wise SY, Carpenter AD, Olsen CH. Determination of lethality curve for cobalt-60 gamma-radiation source in Rhesus Macaques using subject-based supportive care. Radiat Res. 2022; 198(6):599–614.  https://doi.org/10.1667/RADE-22-00101.1Google Scholar

35.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich J. The ultimate qPCR experiment: Producing publication quality, reproducible data the first time. Trends Biotechnol. 2019; 37(7):761–74.  https://doi.org/10.1016/j.tibtech.2018.12.002Google Scholar

36.

Schüle S, Muhtadi R, Stewart S, Asang C, Pleimes D, Stroszczynski C, et al. Using chemotherapy patients as a surrogate cohort for validation of four genes predictive for radiation-induced hematological damage severity. Radiat Res. 2024 (in press). Google Scholar

37.

Cruz-Garcia L, Nasser F, O'Brien G, Grepl J, Vinnikov V, Starenkiy V, et al. Transcriptional dynamics of DNA damage responsive genes in circulating leukocytes during radiotherapy. Cancers (Basel). 2022; 14(11).  https://doi.org/10.3390/cancers14112649Google Scholar

38.

Singh VK, Carpenter AD, Janocha BL, Petrus SA, Fatanmi OO, Wise SY, et al. Radiosensitivity of rhesus nonhuman primates: consideration of sex, supportive care, body weight, and age at time of exposure. Expert Opin Drug Discov 2023; 18(7):797–814.  https://doi.org/10.1080/17460441.2023.2205123Google Scholar

39.

Schule S, Gluzman-Poltorak Z, Vainstein V, Basile LA, Haimerl M, Stroszczynski C, et al. Gene expression changes in a prefinal health stage of lethally irradiated male and female rhesus macaques. Radiat Res 2023; 199(1):17–24.  https://doi.org/10.1667/RADE-22-00083.1Google Scholar

40.

Abend M, Amundson SA, Badie C, Brzoska K, Hargitai R, Kriehuber R, et al. Inter-laboratory comparison of gene expression biodosimetry for protracted radiation exposures as part of the RENEB and EURADOS WG10 2019 exercise. Sci Rep 2021; 11(1):9756.  https://doi.org/10.1038/s41598-021-88403-4Google Scholar

41.

Abend M, Amundson SA, Badie C, Brzoska K, Kriehuber R, Lacombe J, et al. RENEB Inter-laboratory comparison 2021: The gene expression assay. Radiat Res 2023; 199(6):598–615.  https://doi.org/10.1667/RADE-22-00206.1 Google Scholar

42.

Port M, Barquinero JF, Endesfelder D, Moquet J, Oestreicher U, Terzoudi G, et al. RENEB Inter-laboratory comparison 2021: Inter-assay comparison of eight dosimetry assays. Radiat Res 2023; 199(6):535–55.  https://doi.org/10.1667/RADE-22-00207.1 Google Scholar
D. Schwanke, S. Schüle, S. Stewart, O. O. Fatanmi, S. Y. Wise, C. Hackenbroch, T. Wiegel, V. K. Singh, M. Port, M. Abend, and P. Ostheim "Validating a Four-gene Set for H-ARS Severity Prediction in Peripheral Blood Samples of Irradiated Rhesus Macaques," Radiation Research 201(5), 504-513, (8 May 2024). https://doi.org/10.1667/RADE-23-00162.1
Received: 11 August 2023; Accepted: 26 October 2023; Published: 8 May 2024
Back to Top