Proteomic Analysis of Plasma at the Preterminal Stage of Rhesus Nonhuman Primates Exposed to a Lethal Total-Body Dose of Gamma-Radiation

Proteomic Analysis of Plasma at the Preterminal Stage of Rhesus Nonhuman Primates Exposed to a Lethal Total-Body Dose of Gamma-Radiation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Proteomic Analysis of Plasma at the Preterminal Stage of Rhesus Nonhuman Primates Exposed to a Lethal Total-Body Dose of Gamma-Radiation Alana D. Carpenter, Oluseyi O. Fatanmi, Stephen Y. Wise, John B. Tyburski, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4190029/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The identification and validation of radiation biomarkers is critical for assessing the radiation dose received in exposed individuals and for developing radiation medical countermeasures that can be used to treat acute radiation syndrome (ARS). Additionally, a fundamental understanding of the effects of radiation injury could further aid in the identification and development of therapeutic targets for mitigating radiation damage. In this study, blood samples were collected from fourteen male nonhuman primates (NHPs) that were exposed to 7.2 Gy ionizing radiation at various time points (seven days prior to irradiation; 1, 13, and 25 days post-irradiation; as well as immediately prior to the euthanasia of moribund animals (preterminal)). Plasma was isolated from these samples and was analyzed using a liquid chromatography tandem mass spectrometry approach in an effort to determine the effects of radiation on plasma proteomic profiles. Of particular interest was to determine if the expression of certain proteins reacted to radiation in a way that would act as a predictor for health decline leading to a preterminal phenotype. Our results suggest that radiation induced a diverse temporal pattern among protein expression that displayed prominent changes within NHP proteomic plasma profiles. Of these significantly altered proteins, several play important roles in certain biological processes such as hemostasis, inflammation, and immune response. Biomarkers gamma-radiation nonhuman primates preterminal proteomics total-body irradiation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Potential exposure to high doses of ionizing radiation is an ever-increasing risk that is compounded by a global push toward clean, nuclear energy as well as by strained international relations between developed countries 1 . Nuclear events are not only detrimental in terms of the catastrophic damage they cause to infrastructure, but are also particularly complicated to manage, both in short and long-term 2 , 3 . Exposure to ionizing radiation damages living tissue directly by inducing double-strand breaks in DNA, or indirectly, by producing free radicals and reactive oxygen species (ROS) 4 . Direct damage to DNA is particularly detrimental as it alters gene expression, which induces a cascade of changes that can be observed downstream in the form of proteomic changes. Currently, there is no way for radiation doses to be accurately assessed in acutely exposed individuals that go on to develop acute radiation syndrome (ARS). Therefore, it is extremely difficult to treat and manage this illness 5 . ARS is challenging to treat not only due to difficulty in assessing absorbed radiation doses, but also due to a prodromal and latent stage that lasts several days or even a few weeks before symptoms manifest 6 , 7 . In an attempt to address these shortcomings in patient care, research has been conducted to identify biomarkers (metabolites, proteins, etc.) in easily attainable samples (plasma or serum from blood samples) that can possibly assist in pinpointing the absorbed radiation dose in exposed individuals or anticipating health decline so appropriate treatments can be administered 8 , 9 . Biomarkers have been at the forefront of discussion and research within radiation biology for several years, as they can potentially exhibit biological processes closely related to the mechanism of disease 4 , 10 , 11 . In radiation exposed individuals, biomarkers that can assess absorbed radiation doses as well as predict health decline are needed so that treatments can be applied that will ultimately improve overall patient outcome 12 – 14 . Biomarkers can be used in the diagnostic, prognostic, predictive, and pharmacodynamic aspects of drug development. One biomarker may play a role in more than one aspect of drug development. A diagnostic biomarker is a disease characteristic that categorizes an individual by the presence or absence of a physiological or pathophysiological state. A prognostic biomarker is a baseline attribute that categorizes victims by degree of risk for disease occurrence or progression of a disease. It is informative about the natural history of the disease in the absence of a therapeutic intervention. A predictive biomarker is a baseline characteristic that categorizes individuals by their likelihood of response to a particular treatment. A change in a pharmacodynamic biomarker indicates that a biological response has occurred in an individual who has received a drug; the magnitude of the change is considered pertinent to the response. From a regulatory viewpoint, biomarkers have been accepted through several ad hoc pathways in drug regulatory agencies. At the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMEA) and the Pharmaceuticals and Medical Devices Agency (PMDA, Japan), biomarkers have been qualified in recent years. Currently, several biomarkers are approved for specific individual injuries; the US FDA has biomarkers for about 150 drug interactions validated, the EMEA has biomarkers for a few injuries approved, and the PMDA also has biomarkers for a few injuries accepted 15 – 17 . However, none of these are biomarkers for radiation injury. Multiple potential biomarkers are in the process of being confirmed, including some with radiation applications 18 – 20 . The current study attempts to elucidate the proteomic and biochemical landscape modulations in the blood plasma of nonhuman primates (NHPs) after exposure to a lethal dose of 7.2 Gy total-body radiation (Fig. 1). Plasma samples were collected pre-irradiation (day − 7), at 1, 13, and 25 days post-irradiation, and immediately prior to death in moribund animals (termed “preterminal” samples) 21 . The comparative analysis of the plasma proteomic profiles at various time points was central to this investigation, as it provides insight into the temporal dynamics of radiation-induced biological alterations. Previous transcriptomic and metabolomic research has determined that there are definitive proteomic signatures in preterminal statuses, which warrant additional investigations 21 , 22 . Our results demonstrate dynamic changes in proteomic expression that evolved from acute to late post-irradiation phases. Interestingly, the preterminal phase was marked by an amplification of specific proteomic changes, indicating heightened biological stress or damage responses, particularly in proteins related to inflammation, hemostasis, and cellular integrity. The findings from this study contribute to a deeper understanding of the temporal progression of radiation injury and may aid in the identification of therapeutic targets for mitigating radiation damage. Results For this study, we aimed to discern proteomic changes induced by radiation exposure by comparing samples collected post-irradiation to the pre-irradiation time point. Additionally, we wanted to determine whether there were significant changes in proteomic samples collected immediately prior to the euthanasia of moribund animals when compared to pre-irradiation and post-irradiation time points. Plasma samples were collected from 14 male NHPs pre-irradiation (day − 7; n = 6), post-irradiation (days 1, 13, and 25; n = 6 for each time point), and immediately prior to humane euthanasia (preterminal; n = 4). Following the analysis and examination of plasma profiles, a stark proteomic contrast was observed between the pre-irradiation time point and the post-irradiation time points. This divergence between time points signifies a clear impact of radiation on the plasma proteome, as reflected in the principal component analysis (PCA) plot by the separate clusters formed by the pre-irradiation group compared to the day 1, day 13, day 25, and preterminal groups (Fig. 2). Radiation induced changes in proteomic profiles The subtle yet definitive changes in proteomic profiles following exposure to ionizing radiation can be viewed in the PCA and volcano plots in Fig. 3. While there is some overlap in the PCA plots comparing pre-irradiation to days 1, 13, and 25 post-irradiation (Fig. 3: panels A, C, and E, respectively), suggesting there are some shared proteomic features, there are also distinct regions where the post-irradiation samples cluster away from the pre-irradiation group, indicating specific proteomic changes induced by radiation. The corresponding volcano plots (Fig. 3: panels B, D, and F) display metabolites that meet significance based on p-value (X-axis) and fold change (Y-axis), and reveal a more granular perspective. The majority of proteins do not display drastic changes in expression; however, there were a few select proteins that cross the threshold of statistical significance and fold change. Significant changes in proteomic profiles were highest at days 1 and 13 post-irradiation (103 and 128 significantly dysregulated proteins, respectively) when comparing to the pre-irradiation time point. By day 25, many of these aberrations resolved, with only 62 dysregulated proteins remaining in surviving animals. Variability in protein expression was observed across all post-irradiation study days analyzed (days 1, 13, and 25). While there is a discernible overlap in the early post-irradiation stages, the distinction becomes more pronounced by the last study day (day 25) in several proteins. For example, there was an upregulation of inter-alpha trypsin inhibitor heavy chain H4, tubulin alpha-3E chain, peptidyl-prolyl cis-trans isomerase D, and keratin type II cytoskeletal 8 in response to radiation exposure, which continued to gradually increase as the study progressed suggesting a correlation with the pre-terminal phenotype (Fig. 4). Other proteins were significantly upregulated at all time points post-irradiation, which included UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 7; lipopolysaccharide-binding protein; keratin, type II cytoskeletal 1b; Fer-1-like protein 4; dynein heavy chain domain-containing protein 1; dihydrolipoyl dehydrogenase, mitochondrial; complement C5; ceruloplasmin; and actin, alpha skeletal muscle (Table 1 ). Table 1 Proteins that were significantly upregulated at all time points post-irradiation when comparing to the pre-irradiation baseline. Day 1 vs. Pre-Irradiation Day 13 vs. Pre-Irradiation Day 25 vs. Pre-Irradiation UniprotID Protein Name Fold Change log2(FC) Fold Change log2(FC) Fold Change log2(FC) Q8NFL0 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 7 5.52 2.46 5.75 2.52 2.47 1.30 P18428 Lipopolysaccharide-binding protein 6.42 2.68 10.79 3.43 6.94 2.79 Q7Z794 Keratin, type II cytoskeletal 1b 2.66 1.41 3.85 1.95 3.42 1.77 A9Z1Z3 Fer-1-like protein 4 2.87 1.52 2.85 1.51 2.21 1.14 Q96M86 Dynein heavy chain domain-containing protein 1 1.68 0.75 1.79 0.84 1.81 0.86 P09622 Dihydrolipoyl dehydrogenase, mitochondrial 3.78 1.92 2.50 1.32 2.69 1.43 P01031 Complement C5 1.57 0.65 1.98 0.99 1.85 0.89 P00450 Ceruloplasmin 2.11 1.08 2.23 1.16 2.28 1.19 P68133 Actin, alpha skeletal muscle 6.18 2.63 5.45 2.45 3.60 1.85 Marked yet variable responses in protein expression were noted in the preterminal state Pronounced and complex proteomic changes were noted as NHPs advanced to the preterminal phase. The PCA visualizations across comparisons with the day 1, day 13, and day 25 time points reveal a clear divergence in proteomic signatures in the preterminal group (Fig. 5: panels A, C, and E). The volcano plots reflect a marked increase in proteins crossing the threshold of significance within the preterminal group, indicating a heightened level of proteomic disruption as the animals approached terminal conditions (Fig. 5: panels B, D, and F). However, the spread of the data points suggests a varied response among proteins with considerable individual variation between animals. As expected, a lesser degree of significance was noted when comparing preterminal samples to the post-irradiation time points, and these significant differences were more pronounced in the later study days (days 13 and 25). Inter-alpha-trypsin inhibitor heavy chain H4, tubulin alpha-3E chain, peptidyl-prolyl cis-trans isomerase D, and keratin type II cytoskeletal 8 expression increased gradually post-irradiation (apart from a decrease in intensity in peptidyl-prolyl cis-trans isomerase D on day 13), with a more marked and distinct increase as animals approached the preterminal state (Fig. 4). Other proteins like immunoglobulin lambda variable 5–48, thrombospondin-4, plasminogen activator inhibitor 1, and integrin alpha-1 followed more unique trends in preterminal status that varied in response, underscoring the complex and varied response to radiation in proteomic profiles (Fig. 6). Discussion The identification and validation of proteomic biomarkers for detection and/or prediction of radiation injury currently represents an unmet medical need. Interrogating longitudinally collected biospecimens pre- and post-irradiation for downstream molecular phenotyping analyses allows for the identification of several potential proteomic biomarkers. These biomarkers are indicators of overall health or decline thereof, and can be leveraged for early interventions and/or to manage ARS in exposed populations. Additionally, once validated, these biomarkers also have tremendous translational ability and many applications including drug development, understanding the effects of radiation on biological systems, and assessing absorbed radiation doses in exposed populations after a nuclear event. Extensive research evaluating the changes within proteomic profiles incited by lethal doses of ionizing radiation has been conducted in our laboratory 23 . Serum samples of irradiated NHPs 24 , 25 , tissue (jejunum) and biofluids (serum) of irradiated mice 26 , 27 , in addition to irradiated CD 34+ cell culture supernatants 28 have been thoroughly evaluated. The radiation sources utilized in our studies contain high level cobalt-60 gamma radiation and various radiation countermeasures under development including tocopherol succinate 27 , gamma-tocotrienol 26 , 27 , BIO 300 24 , and Ex-Rad 25 . Tocopherol succinate and gamma-tocotrienol have been evaluated in murine models 26 , 27 (tocopherol succinate was also investigated using CD 34+ cells in vitro 28 ), while BIO 300 and Ex-Rad have been investigated using NHP models 24 , 25 . To assess these proteomic changes, methods including NanoUPLC-MS/MS 24 , 25 , two-dimensional differential in-gel electrophoresis (2D-DIGE) 26 , 27 , and a high throughput antibody microarray platform 28 have been used. In this study, we aimed to characterize the proteomic changes induced by 7.2 Gy total-body irradiation by comparing samples collected before irradiation to samples collected post-irradiation at pre-selected time points (days 1, 13, and 25 post-irradiation). Plasma samples were also collected from moribund animals immediately prior to humane euthanasia; in this study, we have termed these samples “preterminal.” These preterminal samples were compared to the pre-irradiation and post-irradiation time points, and offer insight into the complex changes that are occurring on a cellular level in animals that are experiencing significant health decline and are on the verge of death. As expected, a lesser degree of significance was noted when comparing preterminal samples to the post-irradiation time points, and these significant differences were more pronounced in the later study days (days 13 and 25). Ultimately, although there was a clear delineation between the pre-irradiation and immediate post-irradiation (day 1) groups, the subsequent time points (day 13 and day 25) demonstrated a trajectory of proteomic alterations, possibly reflecting a biological adaptation or progression of radiation-induced effects. A deeper analysis revealed that radiation induced significant changes in inflammatory, hemostatic, and cellular structural proteins, suggesting these classes of proteins are detrimentally affected by radiation exposure, confirming previous research in which these radiation-induced changes are well-documented. Radiation induces acute damage in both immune and hematopoietic cells, contributing to the development of ARS. However, the long-term immunological effects of radiation on the immune and hematopoietic systems are lesser known 29 , 30 . Additionally, it has also been established that radiation has detrimental effects on the cell membrane, and this damage in turn initiates cellular apoptosis via signaling events 31 . However, heterogeneity in protein responses underscores the complexity of the NHP plasma proteome's reaction to radiation and the influence of individual physiological variability. In other words, a few proteins displayed consistent patterns in intensities post-irradiation, while others followed more unique trends in irradiated animals. Inter-alpha-trypsin inhibitor heavy chain H4, for example, plays an important role in inflammatory responses 32 , 33 . The trajectory of expression in this protein showed a strong positive correlation with proteomic changes in the preterminal phase, in a time dependent manner, suggesting heightened biological stress or damage responses. The effect of radiation on protein expression varied greatly in terms of patterns in up and downregulation, which further underscores the complex and varied response to radiation, and suggests a cascade of biological events leading to a unique proteomic signature associated with the preterminal state. This disparity not only confirms the immediate effects of radiation but also indicates a progressive and compounded proteomic alteration over time, culminating in a distinct preterminal proteomic signature. These insights provide a valuable framework for understanding the progression of radiation effects on a proteomic level and aid in identifying potential biomarkers that could signal the beginning of the transition to critical health stages in irradiated organisms. We have also performed metabolomics analysis on plasma samples collected throughout the course of this study at the same time points, which also demonstrated that radiation induced significant time-dependent metabolic perturbations when compared to pre-irradiation profiles. A distinguishable preterminal phenotype was observed, with notable dysregulation in metabolites related to the glycerophospholipid metabolism and steroid hormone biosynthesis and metabolism pathways 21 . Notably, metabolomic and proteomic preterminal signatures were demonstrated in both of these studies. Although our results provide a strong proof of concept for delineation of protein biomarkers of the pre-terminal state, ultimately, continued research into the preterminal state of moribund NHPs is needed to further identify and validate proteins and pathways that can be targeted for the development of various therapeutic strategies to treat ARS. To this end, an ongoing study in our laboratory using similar preterminal samples from a large number of NHPs irradiated with two separate doses of cobalt-60 gamma-radiation, will allow for the validation of this study’s results. Materials and Methods Experimental Design The primary objective of this proteomic investigation was to discern changes in NHP plasma profiles in samples collected pre and post-exposure to 7.2 Gy total-body gamma-radiation. Preterminal samples were also collected from moribund NHPs immediately prior to euthanasia, and were compared to the pre-irradiation and post-irradiation time points. The experimental design of this study is presented in Fig. 1. Animals A total of 14 male NHPs ( Macaca mulatta , age 3.0 to 5.3 years and weight 3.89 to 6.34 kg) were used in this study. These animals were procured from the National Institutes of Health Animal Center located in Poolesville, MD. These NHPs were housed in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-International and underwent quarantine for seven weeks. Details of animal care are described earlier 21 . The study design and animal procedures were approved by The Institutional Animal Care and Use Committee and the Department of Defense Animal Care and Use Review Office (ACURO). All animal procedures strictly adhered to the Guide for the Care and Use of Laboratory Animals throughout this study as described earlier 34 , 35 . This study was carried out in compliance with the ARRIVE guideline. Irradiation Animals were organized into groups for radiation exposure. The groups were then paired based on the similarities of their abdominal lateral separation measurements (+/- 1 cm). These measurements were precisely obtained utilizing a digital caliper at the core of the abdomen. Animals whose abdomens were not measured within 1 cm of another animal's measurements were irradiated individually. All NHPs underwent a fasting period 18 h prior to radiation exposure, in order to mitigate the risk of radiation-induced vomiting. Animals were then sedated 15 minutes prior with 10–15 mg/kg of ketamine hydrochloride (100 mg/ml) injected intramuscularly ( im ). Thereafter, animals were placed in custom-made Plexiglas restraint boxes and secured. If needed, NHPs were administered a booster (0.1–0.3 ml im ) of ketamine prior to irradiation to reduce potential movement. Positioned in opposite directions of the irradiation platform, two NHPs were both exposed to cobalt-60 total-body gamma radiation at a dose of 7.2 Gy (dose rate of 0.6 Gy/min) 36 . Animals were irradiated between 8:00 AM and 12:00 PM. Following irradiation, animals were returned to their home cages and closely monitored until recovering from sedation. Additional details of TBI are given in earlier publications 37 , 38 . For dosimetry, the alanine/electron paramagnetic resonance (EPR) system was employed, and is recognized as the most precise and accurate methods for measuring high radiation doses 39 – 41 . Cage-side animal observations During the quarantine and study periods, cage-side observations of animals were preformed twice daily, once in the morning and once in the afternoon. Between days 10 to 20 post-irradiation, animals were observed three times a day approximately 6–8 hours apart. Animals that met the criteria for euthanasia outlined in the study protocol were euthanized under the attending veterinarian’s suggestion. Several parameters were used as guidelines for moribundity including inappetence, severe anemia, weakness, minimal or no response to stimuli, etc. 35 . Blood sample collection Blood was collected through a peripheral vessel (via the saphenous or cephalic vein) on days − 7, 1, 13, and 25, as well as immediately prior to the euthanasia (preterminal) of moribund animals, as previously discussed 42 . A 3 ml disposable luer-lock syringe with a 25-gauge needle was used to collect one ml of blood in an ethylenediaminetetraacetic acid (EDTA) tube. Samples were then centrifuged, and plasma was collected. Euthanasia Although the selected study period was scheduled for 60 days, a couple of animals became moribund during the course of the study as a result of the LD 70/60 radiation dose that was used (7.2 Gy total-body exposure). Euthanasia of the moribund animals was performed by a board-certified veterinarian in order to minimize pain and suffering. Animals were euthanized following the American Veterinary Medical Association (AVMA) guidelines 39 , 43 . To prepare for euthanasia, animals were sedated with Ketamine hydrochloride (5–15 mg/kg, im ) injection. Euthanasia was performed by sodium pentobarbital administered intravenously (> 100 mg/kg, Euthasol, Virbac AH, Inc, Fort Worth, TX). Death was confirmed by cessation of pulse, heartbeat, and breathing. Plasma sample preparation The Enrich iST 96X sample kit was used to produce the sample in accordance with the PreOmics manufacturer's instructions. To summarize, 25 µL of EN-Beads were rinsed three times, and 20 µL of plasma was combined with 80 µL of EN-BIND buffer inside the EN-beads. The mixture was then incubated for 30 minutes at 30 ºC and 1200 rpm. Following the three washing stages with the magnetic plate, 50 µL of LYSE-BCT was added to each bead pellet. The beads were then heated to 95°C for 10 minutes while being shaken at 1000 rpm to reduce disulfide bridges, alkylate cysteines, and denature proteins. Following a 5-minute room temperature cooling phase, the mixture was supplemented with Trypsin and LysC, and the proteins were digested for one hour at 37°C. The "Stop" solution was added to halt digestion, and three rounds of washing and elution into the collection plate using the supplied solutions followed to achieve peptide purification. Centrifugation was carried out for three minutes at 2250g. According to the manufacturer's recommendations (ThermoFisher), peptides were measured using the Quantitative Fluorometric Peptide Assay, transferred to low-bind tubes, dried in a vacuum centrifuge, and then an estimated 500 ng of peptide per sample was resuspended in water with 0.1% FA for MS analysis. High-pH Reverse-Phase Fractionation for Library Generation To generate plasma proteome libraries, pools for each plasma sample were generated and pool plasma prepared according to the procedure above. The peptides were fractionated using the Pierce™ High pH Reversed-Phase Peptide Fractionation Kit into 10 fractions as described previously to generate deep proteomes. Peptides quantified using Quantitative Fluorometric Peptide Assay according to manufacture instructions (ThermoFisher) were transferred to low bind tubes, dried in a vacuum centrifuge, and an estimate of 500 ng of peptide per fractions was mixed resuspended in water with 0.1% FA for MS analysis. LC-MS/MS in DDA-PASEF and diaPASEF modes Peptides from the individual fractions, were separated by using a nanoElute 2 (Bruker Daltonik Scientific) coupled on-line to a timsTOF HT mass spectrometer (Bruker Daltonik). Peptides were analytically separated on a PepSep25 column (75 µm × 25 cm, 1.5 µm, C18) and heated to 50°C at a flow rate of 400 nl/min. LC mobile phases A and B were water with 0.1% FA (v/v) and ACN with 0.1% FA (v/v), respectively. The nanoLC was coupled to the timsTOF Pro via a modified nanoelectrospray ion source (Captive Spray II; Bruker Daltonik). Initially, 90 min gradient for the fractionated peptides from QC samples were separated. Data acquisition on the timsTOF HT was performed using TIMSControl 6.0 (Bruker Daltonik) in DDA_PASEF method with following parameter: accumulation and ramp time were set to 100 ms each. Mass spectra were recorded in the range from m/z 100 to 1700. The ion mobility was scanned from 0.85 to 1.35 (V·s)/cm2. Precursors for data-dependent acquisition were isolated within ± 1 Th and fragmented with an ion mobility dependent collision energy, which was linearly increased from 20 to 59 eV. The overall acquisition cycle of 1.17 s comprised one full TIMS-MS scan and 10 parallel accumulation serial fragmentation (PASEF) MS/MS scans. Proteomics data from each fraction samples were analyzed in Realtime PaSER software, searched against the human Swiss-Prot database with the species taxonomy set to Homosapiens. These files were used to generate spectral library for dia_PASEF method. For diaPASEF acquisition, the capillary voltage was set to 1600 V. The MS1 and MS2 spectra were obtained over a mass-to-charge (m/z) range of 100–1700 Th, with an ion mobility range (1/K0) of 0.8–1.3 Vs/cm2. The other setting was the same as DDA-PASEF mode. Additionally, a 28 Th width. Isolation windows were associated with ion mobility windows of 0.3 1/K0 to cover as close as possible the peptide-ions distribution on both m/z and mobility dimensions. Raw data of DIA were processed against the spectral library created from DDA-PASEF mode. General dia-PASEF Analysis Protein identification and quantification analysis were done with PaSER (2023, v 3.0, Bruker Scientific LLC, Billerica, MA, http://www.bruker.com ) using TIMS DIA-NN. Mass spectra were streamed via the PaSER plugin directly from the timsTOF’s acquisition control software (timsControl) to the PaSER workstation via a dedicated LAN connection and pre-processed into a binary file for consumption by TIMS DIA-NN. A spectral library consisting of precursors, including peptide modification such as phosphorylated and acetylated was re-annotated against Uniprot human protein database (downloaded on 01-01-2023) plus sequences of known contaminants such as keratin and porcine trypsin. 20 ppm precursor tolerance and 15 ppm fragment ion tolerance were used along with Top 3 precursors for quantitation. Multiple samples were assembled and match-between-runs performed to fill-in missing values with an outlier frequency of 0.2 following which global normalization was performed. Statistical Analysis To evaluate the effects of 7.2 Gy total-body radiation on NHP plasma profiles, protein abundance that is represented by normalized intensity units was compared. A comprehensive list of all proteins screened for in this study can be viewed in Supplementary Table 1. The blood plasma profiles collected at various timepoints (day 1, day 13, or day 25) were compared to samples collected pre-irradiation and immediately prior to euthanasia (preterminal). Both independent (unpaired) and dependent (paired) statistical tests were performed, and these results can be viewed in Supplementary Tables 2 and 3. Additionally, Supplementary Table 4 combines all comparisons for a holistic view across all groups and time points. For nonparametric data analysis, Mann-Whitney U tests were performed for unpaired comparisons, while for paired comparisons, the Wilcoxon signed-rank test was utilized. A p-value of less than 0.05 was considered statistically significant. Additionally, in an effort to address the issue of multiple comparisons that can potentially increase the likelihood of false positives, a False Discovery Rate (FDR) method was applied to adjust p-values. A more detailed summary of the statistical analyses used to analyze this data has been discussed in a recently published paper 21 . Declarations Acknowledgements: The authors would like to thank the staff of the Radiation Science Department for dosimetry and radiation exposure to the animals, and to the staff of Veterinary Science Department for animal care. The authors would like to acknowledge the Mass Spectrometry and Analytical Pharmacology Shared Resource in Georgetown University (Washington, DC, USA) partially supported by NIH/NCI/CCSG grant P30-CA051008. We are thankful to Folade Olabisi for assistance in revising manuscript content. 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. Author contributions: Study design: VKS; Performance of the study: OOF, SYW, VKS; Data acquisition, curation and analysis: VKS, AKC, ADC, OOF, SYW, JBT; Drafting of the manuscript: ADC, VKS, AKC, OOF, SYW; Revision of manuscript content: VKS, AKC, ADC, OOF, SYW; Supervision: VKS; Funding acquisition: VKS. All authors have read and approved the final submitted manuscript. Funding: The authors gratefully acknowledge the research support from the Uniformed Services University of the Health Sciences/Armed Forces Radiobiology Research Institute (grant # AFR-B4-10978 and 12080) to VKS. Ethical Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by The Institutional Animal Care and Use Committee - Armed Forces Radiobiology Research Institute Approval Code: 2015-12-010, Approval Date: February 24, 2016. Department of Defense second tier approval: Department of Defense Animal Care and Use Review Office (ACURO) Approval Code: 2015-12-010, Approval Date: March 02, 2016. Informed Consent Statement: Not applicable. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Conflicts of Interest: John B. Tyburski is employee of Nelson Scientific Labs, LLC, Potomac. The paper reflects the views of the scientists, and not the company. Other authors declare no conflict of interest. References Gale, R. P., Armitage, J. O. & Hashmi, S. K. Emergency response to radiological and nuclear accidents and incidents. British journal of haematology 192 , 968-972, doi:10.1111/bjh.16138 (2021). Fliedner, T. M. et al. Radiologic and nuclear events: the METREPOL severity of effect grading system. Blood 111 , 5757-5758; author reply 5758-5759, doi:10.1182/blood-2008-04-150243 (2008). Weinstock, D. M. et al. Radiologic and nuclear events: contingency planning for hematologists/oncologists. Blood 111 , 5440-5445, doi:10.1182/blood-2008-01-134817 (2008). Singh, V. K., Newman, V. L., Romaine, P. L., Hauer-Jensen, M. & Pollard, H. B. Use of biomarkers for assessing radiation injury and efficacy of countermeasures. Expert review of molecular diagnostics 16 , 65-81, doi:10.1586/14737159.2016.1121102 (2016). Gale, R. P. & Armitage, J. O. Are we prepared for nuclear terrorism? The New England journal of medicine 378 , 1246-1254, doi:10.1056/NEJMsr1714289 (2018). Donnelly, E. H. et al. Acute radiation syndrome: assessment and management. Southern medical journal 103 , 541-546, doi:10.1097/SMJ.0b013e3181ddd571 (2010). Singh, V. K. & Seed, T. M. in Physician's Guide to Terrorist Attack (ed Michael J Roy) Ch. 24, 339-362 (Humana Press, 2003). Aryankalayil, M. et al. Biomarkers for Biodosimetry and Their Role in Predicting Radiation Injury. Cytogenetic and genome research 163 , 103-109, doi:10.1159/000531444 (2023). Rana, S., Kumar, R., Sultana, S. & Sharma, R. K. Radiation-induced biomarkers for the detection and assessment of absorbed radiation doses. Journal of pharmacy & bioallied sciences 2 , 189-196, doi:10.4103/0975-7406.68500 (2010). Winters, T. A., Taliaferro, L. P. & Satyamitra, M. M. Development of Biomarkers for Radiation Biodosimetry and Medical Countermeasures Research: Current Status, Utility, and Regulatory Pathways. Radiat Res 197 , 554-558, doi:10.1667/rade-21-00213.1 (2022). Califf, R. M. Biomarker definitions and their applications. Experimental biology and medicine (Maywood, N.J 243 , 213-221, doi:10.1177/1535370217750088 (2018). Manna, S. K., Krausz, K. W., Bonzo, J. A., Idle, J. R. & Gonzalez, F. J. Metabolomics reveals aging-associated attenuation of noninvasive radiation biomarkers in mice: potential role of polyamine catabolism and incoherent DNA damage-repair. Journal of proteome research 12 , 2269-2281, doi:10.1021/pr400161k (2013). Chen, Z. et al. Rapid and high-throughput detection and quantitation of radiation biomarkers in human and nonhuman primates by differential mobility spectrometry-mass spectrometry. Journal of the American Society for Mass Spectrometry 27 , 1626-1636, doi:10.1007/s13361-016-1438-5 (2016). Rutten, E. A. & Badie, C. Radiation biomarkers: Silver bullet, or wild goose chase? J Pers Med 11 , doi:10.3390/jpm11070603 (2021). U.S. Food and Drug Administration. Table of pharmacogenomic biomarkers in drug labeling. 2015. Available at: http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm [Last accessed October 25, 2015] European Medicines Agency. Qualification of novel methodologies for medicine development. 2015. Available at: http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp&mid=WC0b01ac0580022bb0 [Last accessed October 25, 2015] Pharmaceutical and Medical Devices Agency. Record of consultations on pharmacogenomics/biomarkers. 2010. Available at: https://www.pmda.go.jp/english/review-services/consultations/0001.html [Last accessed October 25, 2015] Chaudhry, M. A. Biomarkers for human radiation exposure. J Biomed Sci 15 , 557-563, doi:10.1007/s11373-008-9253-z (2008). Kang, C. M. et al. Possible biomarkers for ionizing radiation exposure in human peripheral blood lymphocytes. Radiat Res 159 , 312-319 (2003). Amundson, S. A. et al. Identification of potential mRNA biomarkers in peripheral blood lymphocytes for human exposure to ionizing radiation. Radiat Res 154 , 342-346 (2000). Carpenter, A. D. et al. Metabolomic changes in plasma of preterminal stage of rhesus nonhuman primates exposed to lethal dose of radiation. Metabolites 14 , 18, doi:10.3390/metabo14010018 (2024). Schule, S. et al. Gene Expression Changes in a Prefinal Health Stage of Lethally Irradiated Male and Female Rhesus Macaques. Radiat Res 199 , 17-24, doi:10.1667/RADE-22-00083.1 (2023). Singh, V. K., Srivastava, M. & Seed, T. M. Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions. Expert Rev Proteomics 20 , 221-246, doi:10.1080/14789450.2023.2263652 (2023). Girgis, M. et al. Comparative proteomic analysis of serum from nonhuman primates administered BIO 300: a promising radiation countermeasure. Sci Rep 10 , 19343, doi:10.1038/s41598-020-76494-4 (2020). Carpenter, A. D. et al. Analysis of the proteomic profile in serum of irradiated nonhuman primates treated with Ex-Rad, a radiation medical countermeasure. Journal of proteome research 22 , 1116-1126, doi:10.1021/acs.jproteome.2c00458 (2023). Rosen, E., Fatanmi, O. O., Wise, S. Y., Rao, V. A. & Singh, V. K. Gamma-tocotrienol, a radiation countermeasure, reverses proteomic changes in serum following total-body gamma irradiation in mice. Sci Rep 12 , 3387, doi:10.1038/s41598-022-07266-5 (2022). Rosen, E., Fatanmi, O. O., Wise, S. Y., Rao, V. A. & Singh, V. K. Tocol prophylaxis for total-body irradiation: A proteomic analysis in murine model. Health Phys 119 , 12-20, doi:10.1097/HP.0000000000001221 (2020). Srivastava, A. et al. Personalized Radioproteomics: Identification of a Protein Biomarker Signature for Preemptive Rescue by Tocopherol Succinate in CD34(+) Irradiated Progenitor Cells Isolated from a Healthy Control Donor. J Proteomics Bioinform 8 , 23-30, doi:10.4172/jpb.1000349 (2015). Macintyre, A. N. et al. Long-term recovery of the adaptive immune system in rhesus macaques after total body irradiation. Advances in radiation oncology 6 , 100677, doi:10.1016/j.adro.2021.100677 (2021). Kamiya, K. et al. Long-term effects of radiation exposure on health. Lancet 386 , 469-478, doi:10.1016/s0140-6736(15)61167-9 (2015). Cohen–Jonathan, E., Bernhard, E. J. & McKenna, W. G. How does radiation kill cells? Curr. Opin. Chem. Biol. 3 , 77-83, doi:https://doi.org/10.1016/S1367-5931(99)80014-3 (1999). Zhao, X., Guo, Y., Li, L. & Li, Y. Longitudinal change of serum inter-alpha-trypsin inhibitor heavy chain H4, and its correlation with inflammation, multiorgan injury, and death risk in sepsis. J. Clin. Lab. Anal. 37 , e24834, doi:10.1002/jcla.24834 (2023). Kashyap, R. S. et al. Inter-alpha-trypsin inhibitor heavy chain 4 is a novel marker of acute ischemic stroke. Clin. Chim. Acta 402 , 160-163, doi:10.1016/j.cca.2009.01.009 (2009). National Research Council of the National Academy of Sciences. Guide for the care and use of laboratory animals . 8th edn, (National Academies Press, 2011). Singh, V. K., Fatanmi, O. O., Wise, S. Y., Carpenter, A. D. & Olsen, C. H. Determination of lethality curve for cobalt-60 gamma-radiation source in rhesus macaques using subject-based supportive care. Radiat Res 198 , 599-614, doi:10.1667/RADE-22-00101.1 (2022). Phipps, A. J., Bergmann, J. N., Albrecht, M. T., Singh, V. K. & Homer, M. J. Model for evaluating antimicrobial therapy to prevent life-threatening bacterial infections following exposure to a medically significant radiation dose. Antimicrobial agents and chemotherapy 66 , e0054622, doi:10.1128/aac.00546-22 (2022). Li, Y. et al. Transcriptome of rhesus macaque ( Macaca mulatta ) exposed to total-body irradiation. Sci Rep 11 , 6295, doi:10.1038/s41598-021-85669-6 (2021). Li, Y. et al. Analysis of the metabolomic profile in serum of irradiated nonhuman primates treated with Ex-Rad, a radiation countermeasure. Sci Rep 11 , 11449, doi:10.1038/s41598-021-91067-9 (2021). Singh, V. K. et al. Radioprotective efficacy of gamma-tocotrienol in nonhuman primates. Radiat Res 185 , 285-298, doi:10.1667/RR14127.1 (2016). International Standardization Organization and ASTM International. in Standard Practice for Use of an Alanine-EPR Dosimetry System. 7 (ASTM International, ISO and West Conshohocken (US:PA):). Nagy, V. Accuracy considerations in EPR dosimetry. Appl Radiat Isot 52 , 1039-1050 (2000). Cheema, A. K. et al. Identification of novel biomarkers for acute radiation injury using multiomics approach and nonhuman primate model. Int J Radiat Oncol Biol Phys 114 , 310-320, doi:10.1016/j.ijrobp.2022.05.046 (2022). American Veterinary Medical Association. AVMA Guidelines for the Euthanasia of Animals: 2020 Edition. 2020. Available at: https://www.avma.org/sites/default/files/2020-01/2020-Euthanasia-Final-1-17-20.pdf [Last accessed December 29, 2022] Additional Declarations Competing interest reported. John B. Tyburski is employee of Nelson Scientific Labs, LLC, Potomac. The paper reflects the views of the scientists, and not the company. Other authors declare no conflict of interest. Supplementary Files 20240329SupplementaryTables14.xlsx Cite Share Download PDF Status: Published Journal Publication published 12 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Apr, 2024 Reviews received at journal 23 Apr, 2024 Reviews received at journal 22 Apr, 2024 Reviewers agreed at journal 11 Apr, 2024 Reviewers agreed at journal 06 Apr, 2024 Reviewers invited by journal 05 Apr, 2024 Editor assigned by journal 05 Apr, 2024 Editor invited by journal 05 Apr, 2024 Submission checks completed at journal 05 Apr, 2024 First submitted to journal 29 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4190029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288616643,"identity":"f3d65522-32d1-4bf4-8976-036b3b81186e","order_by":0,"name":"Alana D. Carpenter","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alana","middleName":"D.","lastName":"Carpenter","suffix":""},{"id":288616644,"identity":"fc483be8-b7d5-473a-aa01-53772571d5ad","order_by":1,"name":"Oluseyi O. Fatanmi","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Oluseyi","middleName":"O.","lastName":"Fatanmi","suffix":""},{"id":288616646,"identity":"03a1dbaf-e00e-4c11-9c5e-fe4c9e0c6b86","order_by":2,"name":"Stephen Y. Wise","email":"","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"Y.","lastName":"Wise","suffix":""},{"id":288616647,"identity":"72fdb288-0e1f-4035-871d-b428afef80f6","order_by":3,"name":"John B. Tyburski","email":"","orcid":"","institution":"Nelson Scientific Labs, LLC","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"B.","lastName":"Tyburski","suffix":""},{"id":288616648,"identity":"453406c3-5469-49a2-8ad2-130e387222d4","order_by":4,"name":"Amrita K. Cheema","email":"","orcid":"","institution":"Georgetown University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Amrita","middleName":"K.","lastName":"Cheema","suffix":""},{"id":288616649,"identity":"31eb1361-cd00-4dff-b0c8-687c7d0cde8b","order_by":5,"name":"Vijay K. Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZoYEEMUDRIwPgAwwT4JYLcwGxGlBAB42CaK0GBxnePiZh6FOxpx/7bFqnj+H8+QbmA/e5sGn5TBDsjQPw2Eeyxnv0m7zth0uNjjAlmyNT4tkM0MCUMsBHoMbZ8xu8zYcTtzAwGMmTUBL8m+gw8BaioEOS5zfwP8NrxZ+ZoY0oAJmHoPzPWbMPGyHExsO8LAR1GI5x+Aw0BYeY8m5bemJGw6zGVvOwaOFjf9M8o03FXX2BufPGH5488c6cX5788Mbb/BoAUZHAhMPKA4lEqACzHiVgwD7AcYfYCceIKh0FIyCUTAKRigAAI3mRoRP/EekAAAAAElFTkSuQmCC","orcid":"","institution":"Uniformed Services University of the Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Vijay","middleName":"K.","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-03-30 00:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4190029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4190029/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-64316-w","type":"published","date":"2024-06-12T14:55:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54445143,"identity":"822fffd4-1a34-4356-8c18-a0767fade248","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":350196,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design to assess changes in plasma proteomics profiles in NHPs exposed to 7.2 Gy TBI from samples collected pre-irradiation (day -7), post-irradiation (day 1, day 13, and day 25), or immediately prior to euthanasia (preterminal).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/e2eb11b42c561e138fcc34dd.png"},{"id":54445142,"identity":"b6532b3a-c65b-4a4a-b7a5-894954213f49","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":551347,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/b015f37bc84b20300d8ec05e.png"},{"id":54446780,"identity":"e250be49-e50b-49d9-879e-2672304a3e8e","added_by":"auto","created_at":"2024-04-10 16:23:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1077789,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/1779155dea6cb1798efd55dd.png"},{"id":54445145,"identity":"7ef2f29e-11eb-4bc8-a621-bdf27006854f","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":307924,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/3cbf7bfcf76662c5eaa08a21.png"},{"id":54445147,"identity":"c1ac9a2b-8bfc-46ca-93f9-d148c5d23f9a","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":913918,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/11796f3ec1875ec1831c5bdb.png"},{"id":54445144,"identity":"bfaa814a-75ad-4aad-a289-572f52a87135","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":333240,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/7cd9782fcd914b17fda70046.png"},{"id":58822404,"identity":"13b84a7c-9ed3-42e4-9803-faf9ffc0d66f","added_by":"auto","created_at":"2024-06-21 16:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4446181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/f40ae0dc-6530-422f-b09a-0cade2f0bbb4.pdf"},{"id":54445148,"identity":"d3257030-7c1b-4155-bb31-7637a0a4b93c","added_by":"auto","created_at":"2024-04-10 16:15:10","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":659896,"visible":true,"origin":"","legend":"","description":"","filename":"20240329SupplementaryTables14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4190029/v1/f73cac14d98717454169e162.xlsx"}],"financialInterests":"Competing interest reported. John B. Tyburski is employee of Nelson Scientific Labs, LLC, Potomac. The paper reflects the views of the scientists, and not the company. Other authors declare no conflict of interest.","formattedTitle":"Proteomic Analysis of Plasma at the Preterminal Stage of Rhesus Nonhuman Primates Exposed to a Lethal Total-Body Dose of Gamma-Radiation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePotential exposure to high doses of ionizing radiation is an ever-increasing risk that is compounded by a global push toward clean, nuclear energy as well as by strained international relations between developed countries \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Nuclear events are not only detrimental in terms of the catastrophic damage they cause to infrastructure, but are also particularly complicated to manage, both in short and long-term \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Exposure to ionizing radiation damages living tissue directly by inducing double-strand breaks in DNA, or indirectly, by producing free radicals and reactive oxygen species (ROS) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Direct damage to DNA is particularly detrimental as it alters gene expression, which induces a cascade of changes that can be observed downstream in the form of proteomic changes. Currently, there is no way for radiation doses to be accurately assessed in acutely exposed individuals that go on to develop acute radiation syndrome (ARS). Therefore, it is extremely difficult to treat and manage this illness \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eARS is challenging to treat not only due to difficulty in assessing absorbed radiation doses, but also due to a prodromal and latent stage that lasts several days or even a few weeks before symptoms manifest\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In an attempt to address these shortcomings in patient care, research has been conducted to identify biomarkers (metabolites, proteins, etc.) in easily attainable samples (plasma or serum from blood samples) that can possibly assist in pinpointing the absorbed radiation dose in exposed individuals or anticipating health decline so appropriate treatments can be administered \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Biomarkers have been at the forefront of discussion and research within radiation biology for several years, as they can potentially exhibit biological processes closely related to the mechanism of disease \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In radiation exposed individuals, biomarkers that can assess absorbed radiation doses as well as predict health decline are needed so that treatments can be applied that will ultimately improve overall patient outcome \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBiomarkers can be used in the diagnostic, prognostic, predictive, and pharmacodynamic aspects of drug development. One biomarker may play a role in more than one aspect of drug development. A \u003cem\u003ediagnostic\u003c/em\u003e biomarker is a disease characteristic that categorizes an individual by the presence or absence of a physiological or pathophysiological state. A \u003cem\u003eprognostic\u003c/em\u003e biomarker is a baseline attribute that categorizes victims by degree of risk for disease occurrence or progression of a disease. It is informative about the natural history of the disease in the absence of a therapeutic intervention. A \u003cem\u003epredictive\u003c/em\u003e biomarker is a baseline characteristic that categorizes individuals by their likelihood of response to a particular treatment. A change in a \u003cem\u003epharmacodynamic\u003c/em\u003e biomarker indicates that a biological response has occurred in an individual who has received a drug; the magnitude of the change is considered pertinent to the response. From a regulatory viewpoint, biomarkers have been accepted through several \u003cem\u003ead hoc\u003c/em\u003e pathways in drug regulatory agencies. At the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMEA) and the Pharmaceuticals and Medical Devices Agency (PMDA, Japan), biomarkers have been qualified in recent years. Currently, several biomarkers are approved for specific individual injuries; the US FDA has biomarkers for about 150 drug interactions validated, the EMEA has biomarkers for a few injuries approved, and the PMDA also has biomarkers for a few injuries accepted \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, none of these are biomarkers for radiation injury. Multiple potential biomarkers are in the process of being confirmed, including some with radiation applications \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe current study attempts to elucidate the proteomic and biochemical landscape modulations in the blood plasma of nonhuman primates (NHPs) after exposure to a lethal dose of 7.2 Gy total-body radiation (Fig.\u0026nbsp;1). Plasma samples were collected pre-irradiation (day \u0026minus;\u0026thinsp;7), at 1, 13, and 25 days post-irradiation, and immediately prior to death in moribund animals (termed \u0026ldquo;preterminal\u0026rdquo; samples) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The comparative analysis of the plasma proteomic profiles at various time points was central to this investigation, as it provides insight into the temporal dynamics of radiation-induced biological alterations. Previous transcriptomic and metabolomic research has determined that there are definitive proteomic signatures in preterminal statuses, which warrant additional investigations \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our results demonstrate dynamic changes in proteomic expression that evolved from acute to late post-irradiation phases. Interestingly, the preterminal phase was marked by an amplification of specific proteomic changes, indicating heightened biological stress or damage responses, particularly in proteins related to inflammation, hemostasis, and cellular integrity. The findings from this study contribute to a deeper understanding of the temporal progression of radiation injury and may aid in the identification of therapeutic targets for mitigating radiation damage.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor this study, we aimed to discern proteomic changes induced by radiation exposure by comparing samples collected post-irradiation to the pre-irradiation time point. Additionally, we wanted to determine whether there were significant changes in proteomic samples collected immediately prior to the euthanasia of moribund animals when compared to pre-irradiation and post-irradiation time points. Plasma samples were collected from 14 male NHPs pre-irradiation (day \u0026minus;\u0026thinsp;7; n\u0026thinsp;=\u0026thinsp;6), post-irradiation (days 1, 13, and 25; n\u0026thinsp;=\u0026thinsp;6 for each time point), and immediately prior to humane euthanasia (preterminal; n\u0026thinsp;=\u0026thinsp;4). Following the analysis and examination of plasma profiles, a stark proteomic contrast was observed between the pre-irradiation time point and the post-irradiation time points. This divergence between time points signifies a clear impact of radiation on the plasma proteome, as reflected in the principal component analysis (PCA) plot by the separate clusters formed by the pre-irradiation group compared to the day 1, day 13, day 25, and preterminal groups (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRadiation induced changes in proteomic profiles\u003c/h2\u003e \u003cp\u003eThe subtle yet definitive changes in proteomic profiles following exposure to ionizing radiation can be viewed in the PCA and volcano plots in Fig.\u0026nbsp;3. While there is some overlap in the PCA plots comparing pre-irradiation to days 1, 13, and 25 post-irradiation (Fig.\u0026nbsp;3: panels A, C, and E, respectively), suggesting there are some shared proteomic features, there are also distinct regions where the post-irradiation samples cluster away from the pre-irradiation group, indicating specific proteomic changes induced by radiation. The corresponding volcano plots (Fig.\u0026nbsp;3: panels B, D, and F) display metabolites that meet significance based on p-value (X-axis) and fold change (Y-axis), and reveal a more granular perspective. The majority of proteins do not display drastic changes in expression; however, there were a few select proteins that cross the threshold of statistical significance and fold change.\u003c/p\u003e \u003cp\u003eSignificant changes in proteomic profiles were highest at days 1 and 13 post-irradiation (103 and 128 significantly dysregulated proteins, respectively) when comparing to the pre-irradiation time point. By day 25, many of these aberrations resolved, with only 62 dysregulated proteins remaining in surviving animals. Variability in protein expression was observed across all post-irradiation study days analyzed (days 1, 13, and 25). While there is a discernible overlap in the early post-irradiation stages, the distinction becomes more pronounced by the last study day (day 25) in several proteins. For example, there was an upregulation of inter-alpha trypsin inhibitor heavy chain H4, tubulin alpha-3E chain, peptidyl-prolyl cis-trans isomerase D, and keratin type II cytoskeletal 8 in response to radiation exposure, which continued to gradually increase as the study progressed suggesting a correlation with the pre-terminal phenotype (Fig.\u0026nbsp;4). Other proteins were significantly upregulated at all time points post-irradiation, which included UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 7; lipopolysaccharide-binding protein; keratin, type II cytoskeletal 1b; Fer-1-like protein 4; dynein heavy chain domain-containing protein 1; dihydrolipoyl dehydrogenase, mitochondrial; complement C5; ceruloplasmin; and actin, alpha skeletal muscle (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProteins that were significantly upregulated at all time points post-irradiation when comparing to the pre-irradiation baseline.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDay 1 vs.\u003c/p\u003e \u003cp\u003ePre-Irradiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDay 13 vs.\u003c/p\u003e \u003cp\u003ePre-Irradiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eDay 25 vs.\u003c/p\u003e \u003cp\u003ePre-Irradiation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniprotID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elog2(FC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elog2(FC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003elog2(FC)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ8NFL0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP18428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipopolysaccharide-binding protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ7Z794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKeratin, type II cytoskeletal 1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA9Z1Z3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFer-1-like protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ96M86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynein heavy chain domain-containing protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP09622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDihydrolipoyl dehydrogenase, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP01031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplement C5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP00450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCeruloplasmin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP68133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActin, alpha skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMarked yet variable responses in protein expression were noted in the preterminal state\u003c/h2\u003e \u003cp\u003ePronounced and complex proteomic changes were noted as NHPs advanced to the preterminal phase. The PCA visualizations across comparisons with the day 1, day 13, and day 25 time points reveal a clear divergence in proteomic signatures in the preterminal group (Fig.\u0026nbsp;5: panels A, C, and E). The volcano plots reflect a marked increase in proteins crossing the threshold of significance within the preterminal group, indicating a heightened level of proteomic disruption as the animals approached terminal conditions (Fig.\u0026nbsp;5: panels B, D, and F). However, the spread of the data points suggests a varied response among proteins with considerable individual variation between animals.\u003c/p\u003e \u003cp\u003eAs expected, a lesser degree of significance was noted when comparing preterminal samples to the post-irradiation time points, and these significant differences were more pronounced in the later study days (days 13 and 25). Inter-alpha-trypsin inhibitor heavy chain H4, tubulin alpha-3E chain, peptidyl-prolyl cis-trans isomerase D, and keratin type II cytoskeletal 8 expression increased gradually post-irradiation (apart from a decrease in intensity in peptidyl-prolyl cis-trans isomerase D on day 13), with a more marked and distinct increase as animals approached the preterminal state (Fig.\u0026nbsp;4). Other proteins like immunoglobulin lambda variable 5\u0026ndash;48, thrombospondin-4, plasminogen activator inhibitor 1, and integrin alpha-1 followed more unique trends in preterminal status that varied in response, underscoring the complex and varied response to radiation in proteomic profiles (Fig.\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe identification and validation of proteomic biomarkers for detection and/or prediction of radiation injury currently represents an unmet medical need. Interrogating longitudinally collected biospecimens pre- and post-irradiation for downstream molecular phenotyping analyses allows for the identification of several potential proteomic biomarkers. These biomarkers are indicators of overall health or decline thereof, and can be leveraged for early interventions and/or to manage ARS in exposed populations. Additionally, once validated, these biomarkers also have tremendous translational ability and many applications including drug development, understanding the effects of radiation on biological systems, and assessing absorbed radiation doses in exposed populations after a nuclear event.\u003c/p\u003e \u003cp\u003eExtensive research evaluating the changes within proteomic profiles incited by lethal doses of ionizing radiation has been conducted in our laboratory \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Serum samples of irradiated NHPs \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, tissue (jejunum) and biofluids (serum) of irradiated mice \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, in addition to irradiated CD\u003csup\u003e34+\u003c/sup\u003e cell culture supernatants \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e have been thoroughly evaluated. The radiation sources utilized in our studies contain high level cobalt-60 gamma radiation and various radiation countermeasures under development including tocopherol succinate \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, gamma-tocotrienol \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, BIO 300 \u003csup\u003e24\u003c/sup\u003e, and Ex-Rad \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Tocopherol succinate and gamma-tocotrienol have been evaluated in murine models \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (tocopherol succinate was also investigated using CD\u003csup\u003e34+\u003c/sup\u003e cells in vitro \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e), while BIO 300 and Ex-Rad have been investigated using NHP models \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To assess these proteomic changes, methods including NanoUPLC-MS/MS \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, two-dimensional differential in-gel electrophoresis (2D-DIGE) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and a high throughput antibody microarray platform \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e have been used.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to characterize the proteomic changes induced by 7.2 Gy total-body irradiation by comparing samples collected before irradiation to samples collected post-irradiation at pre-selected time points (days 1, 13, and 25 post-irradiation). Plasma samples were also collected from moribund animals immediately prior to humane euthanasia; in this study, we have termed these samples \u0026ldquo;preterminal.\u0026rdquo; These preterminal samples were compared to the pre-irradiation and post-irradiation time points, and offer insight into the complex changes that are occurring on a cellular level in animals that are experiencing significant health decline and are on the verge of death.\u003c/p\u003e \u003cp\u003eAs expected, a lesser degree of significance was noted when comparing preterminal samples to the post-irradiation time points, and these significant differences were more pronounced in the later study days (days 13 and 25). Ultimately, although there was a clear delineation between the pre-irradiation and immediate post-irradiation (day 1) groups, the subsequent time points (day 13 and day 25) demonstrated a trajectory of proteomic alterations, possibly reflecting a biological adaptation or progression of radiation-induced effects.\u003c/p\u003e \u003cp\u003eA deeper analysis revealed that radiation induced significant changes in inflammatory, hemostatic, and cellular structural proteins, suggesting these classes of proteins are detrimentally affected by radiation exposure, confirming previous research in which these radiation-induced changes are well-documented. Radiation induces acute damage in both immune and hematopoietic cells, contributing to the development of ARS. However, the long-term immunological effects of radiation on the immune and hematopoietic systems are lesser known\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, it has also been established that radiation has detrimental effects on the cell membrane, and this damage in turn initiates cellular apoptosis via signaling events\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, heterogeneity in protein responses underscores the complexity of the NHP plasma proteome's reaction to radiation and the influence of individual physiological variability. In other words, a few proteins displayed consistent patterns in intensities post-irradiation, while others followed more unique trends in irradiated animals. Inter-alpha-trypsin inhibitor heavy chain H4, for example, plays an important role in inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The trajectory of expression in this protein showed a strong positive correlation with proteomic changes in the preterminal phase, in a time dependent manner, suggesting heightened biological stress or damage responses.\u003c/p\u003e \u003cp\u003eThe effect of radiation on protein expression varied greatly in terms of patterns in up and downregulation, which further underscores the complex and varied response to radiation, and suggests a cascade of biological events leading to a unique proteomic signature associated with the preterminal state. This disparity not only confirms the immediate effects of radiation but also indicates a progressive and compounded proteomic alteration over time, culminating in a distinct preterminal proteomic signature. These insights provide a valuable framework for understanding the progression of radiation effects on a proteomic level and aid in identifying potential biomarkers that could signal the beginning of the transition to critical health stages in irradiated organisms.\u003c/p\u003e \u003cp\u003eWe have also performed metabolomics analysis on plasma samples collected throughout the course of this study at the same time points, which also demonstrated that radiation induced significant time-dependent metabolic perturbations when compared to pre-irradiation profiles. A distinguishable preterminal phenotype was observed, with notable dysregulation in metabolites related to the glycerophospholipid metabolism and steroid hormone biosynthesis and metabolism pathways \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Notably, metabolomic and proteomic preterminal signatures were demonstrated in both of these studies. Although our results provide a strong proof of concept for delineation of protein biomarkers of the pre-terminal state, ultimately, continued research into the preterminal state of moribund NHPs is needed to further identify and validate proteins and pathways that can be targeted for the development of various therapeutic strategies to treat ARS. To this end, an ongoing study in our laboratory using similar preterminal samples from a large number of NHPs irradiated with two separate doses of cobalt-60 gamma-radiation, will allow for the validation of this study\u0026rsquo;s results.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Design\u003c/h2\u003e \u003cp\u003eThe primary objective of this proteomic investigation was to discern changes in NHP plasma profiles in samples collected pre and post-exposure to 7.2 Gy total-body gamma-radiation. Preterminal samples were also collected from moribund NHPs immediately prior to euthanasia, and were compared to the pre-irradiation and post-irradiation time points. The experimental design of this study is presented in Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eA total of 14 male NHPs (\u003cem\u003eMacaca mulatta\u003c/em\u003e, age 3.0 to 5.3 years and weight 3.89 to 6.34 kg) were used in this study. These animals were procured from the National Institutes of Health Animal Center located in Poolesville, MD. These NHPs were housed in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-International and underwent quarantine for seven weeks. Details of animal care are described earlier \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The study design and animal procedures were approved by The Institutional Animal Care and Use Committee and the Department of Defense Animal Care and Use Review Office (ACURO). All animal procedures strictly adhered to the \u003cem\u003eGuide for the Care and Use of Laboratory Animals\u003c/em\u003e throughout this study as described earlier \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This study was carried out in compliance with the ARRIVE guideline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIrradiation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAnimals were organized into groups for radiation exposure. The groups were then paired based on the similarities of their abdominal lateral separation measurements (+/- 1 cm). These measurements were precisely obtained utilizing a digital caliper at the core of the abdomen. Animals whose abdomens were not measured within 1 cm of another animal's measurements were irradiated individually.\u003c/p\u003e \u003cp\u003eAll NHPs underwent a fasting period 18 h prior to radiation exposure, in order to mitigate the risk of radiation-induced vomiting. Animals were then sedated 15 minutes prior with 10\u0026ndash;15 mg/kg of ketamine hydrochloride (100 mg/ml) injected intramuscularly (\u003cem\u003eim\u003c/em\u003e). Thereafter, animals were placed in custom-made Plexiglas restraint boxes and secured. If needed, NHPs were administered a booster (0.1\u0026ndash;0.3 ml \u003cem\u003eim\u003c/em\u003e) of ketamine prior to irradiation to reduce potential movement. Positioned in opposite directions of the irradiation platform, two NHPs were both exposed to cobalt-60 total-body gamma radiation at a dose of 7.2 Gy (dose rate of 0.6 Gy/min) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnimals were irradiated between 8:00 AM and 12:00 PM. Following irradiation, animals were returned to their home cages and closely monitored until recovering from sedation. Additional details of TBI are given in earlier publications \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. For dosimetry, the alanine/electron paramagnetic resonance (EPR) system was employed, and is recognized as the most precise and accurate methods for measuring high radiation doses \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCage-side animal observations\u003c/h2\u003e \u003cp\u003eDuring the quarantine and study periods, cage-side observations of animals were preformed twice daily, once in the morning and once in the afternoon. Between days 10 to 20 post-irradiation, animals were observed three times a day approximately 6\u0026ndash;8 hours apart. Animals that met the criteria for euthanasia outlined in the study protocol were euthanized under the attending veterinarian\u0026rsquo;s suggestion. Several parameters were used as guidelines for moribundity including inappetence, severe anemia, weakness, minimal or no response to stimuli, etc. \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBlood sample collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBlood was collected through a peripheral vessel (via the saphenous or cephalic vein) on days \u0026minus;\u0026thinsp;7, 1, 13, and 25, as well as immediately prior to the euthanasia (preterminal) of moribund animals, as previously discussed \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. A 3 ml disposable luer-lock syringe with a 25-gauge needle was used to collect one ml of blood in an ethylenediaminetetraacetic acid (EDTA) tube. Samples were then centrifuged, and plasma was collected.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEuthanasia\u003c/h2\u003e \u003cp\u003eAlthough the selected study period was scheduled for 60 days, a couple of animals became moribund during the course of the study as a result of the LD\u003csub\u003e70/60\u003c/sub\u003e radiation dose that was used (7.2 Gy total-body exposure). Euthanasia of the moribund animals was performed by a board-certified veterinarian in order to minimize pain and suffering. Animals were euthanized following the American Veterinary Medical Association (AVMA) guidelines \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. To prepare for euthanasia, animals were sedated with Ketamine hydrochloride (5\u0026ndash;15 mg/kg, \u003cem\u003eim\u003c/em\u003e) injection. Euthanasia was performed by sodium pentobarbital administered intravenously (\u0026gt;\u0026thinsp;100 mg/kg, Euthasol, Virbac AH, Inc, Fort Worth, TX). Death was confirmed by cessation of pulse, heartbeat, and breathing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePlasma sample preparation\u003c/h2\u003e \u003cp\u003eThe Enrich iST 96X sample kit was used to produce the sample in accordance with the PreOmics manufacturer's instructions. To summarize, 25 \u0026micro;L of EN-Beads were rinsed three times, and 20 \u0026micro;L of plasma was combined with 80 \u0026micro;L of EN-BIND buffer inside the EN-beads. The mixture was then incubated for 30 minutes at 30 \u0026ordm;C and 1200 rpm. Following the three washing stages with the magnetic plate, 50 \u0026micro;L of LYSE-BCT was added to each bead pellet. The beads were then heated to 95\u0026deg;C for 10 minutes while being shaken at 1000 rpm to reduce disulfide bridges, alkylate cysteines, and denature proteins.\u003c/p\u003e \u003cp\u003eFollowing a 5-minute room temperature cooling phase, the mixture was supplemented with Trypsin and LysC, and the proteins were digested for one hour at 37\u0026deg;C. The \"Stop\" solution was added to halt digestion, and three rounds of washing and elution into the collection plate using the supplied solutions followed to achieve peptide purification. Centrifugation was carried out for three minutes at 2250g. According to the manufacturer's recommendations (ThermoFisher), peptides were measured using the Quantitative Fluorometric Peptide Assay, transferred to low-bind tubes, dried in a vacuum centrifuge, and then an estimated 500 ng of peptide per sample was resuspended in water with 0.1% FA for MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHigh-pH Reverse-Phase Fractionation for Library Generation\u003c/h2\u003e \u003cp\u003e To generate plasma proteome libraries, pools for each plasma sample were generated and pool plasma prepared according to the procedure above. The peptides were fractionated using the Pierce\u0026trade; High pH Reversed-Phase Peptide Fractionation Kit into 10 fractions as described previously to generate deep proteomes. Peptides quantified using Quantitative Fluorometric Peptide Assay according to manufacture instructions (ThermoFisher) were transferred to low bind tubes, dried in a vacuum centrifuge, and an estimate of 500 ng of peptide per fractions was mixed resuspended in water with 0.1% FA for MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS/MS in DDA-PASEF and diaPASEF modes\u003c/h2\u003e \u003cp\u003ePeptides from the individual fractions, were separated by using a nanoElute 2 (Bruker Daltonik Scientific) coupled on-line to a timsTOF HT mass spectrometer (Bruker Daltonik). Peptides were analytically separated on a PepSep25 column (75 \u0026micro;m \u0026times; 25 cm, 1.5 \u0026micro;m, C18) and heated to 50\u0026deg;C at a flow rate of 400 nl/min. LC mobile phases A and B were water with 0.1% FA (v/v) and ACN with 0.1% FA (v/v), respectively. The nanoLC was coupled to the timsTOF Pro via a modified nanoelectrospray ion source (Captive Spray II; Bruker Daltonik). Initially, 90 min gradient for the fractionated peptides from QC samples were separated. Data acquisition on the timsTOF HT was performed using TIMSControl 6.0 (Bruker Daltonik) in DDA_PASEF method with following parameter: accumulation and ramp time were set to 100 ms each. Mass spectra were recorded in the range from m/z 100 to 1700. The ion mobility was scanned from 0.85 to 1.35 (V\u0026middot;s)/cm2. Precursors for data-dependent acquisition were isolated within \u0026plusmn;\u0026thinsp;1 Th and fragmented with an ion mobility dependent collision energy, which was linearly increased from 20 to 59 eV. The overall acquisition cycle of 1.17 s comprised one full TIMS-MS scan and 10 parallel accumulation serial fragmentation (PASEF) MS/MS scans.\u003c/p\u003e \u003cp\u003eProteomics data from each fraction samples were analyzed in Realtime PaSER software, searched against the human Swiss-Prot database with the species taxonomy set to \u003cem\u003eHomosapiens.\u003c/em\u003e These files were used to generate spectral library for dia_PASEF method.\u003c/p\u003e \u003cp\u003eFor diaPASEF acquisition, the capillary voltage was set to 1600 V. The MS1 and MS2 spectra were obtained over a mass-to-charge (m/z) range of 100\u0026ndash;1700 Th, with an ion mobility range (1/K0) of 0.8\u0026ndash;1.3 Vs/cm2. The other setting was the same as DDA-PASEF mode. Additionally, a 28 Th width. Isolation windows were associated with ion mobility windows of 0.3 1/K0 to cover as close as possible the peptide-ions distribution on both m/z and mobility dimensions. Raw data of DIA were processed against the spectral library created from DDA-PASEF mode.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGeneral dia-PASEF Analysis\u003c/h2\u003e \u003cp\u003eProtein identification and quantification analysis were done with PaSER (2023, v 3.0, Bruker Scientific LLC, Billerica, MA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bruker.com\u003c/span\u003e\u003cspan address=\"http://www.bruker.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using TIMS DIA-NN. Mass spectra were streamed via the PaSER plugin directly from the timsTOF\u0026rsquo;s acquisition control software (timsControl) to the PaSER workstation via a dedicated LAN connection and pre-processed into a binary file for consumption by TIMS DIA-NN. A spectral library consisting of precursors, including peptide modification such as phosphorylated and acetylated was re-annotated against Uniprot human protein database (downloaded on 01-01-2023) plus sequences of known contaminants such as keratin and porcine trypsin. 20 ppm precursor tolerance and 15 ppm fragment ion tolerance were used along with Top 3 precursors for quantitation. Multiple samples were assembled and match-between-runs performed to fill-in missing values with an outlier frequency of 0.2 following which global normalization was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the effects of 7.2 Gy total-body radiation on NHP plasma profiles, protein abundance that is represented by normalized intensity units was compared. A comprehensive list of all proteins screened for in this study can be viewed in Supplementary Table\u0026nbsp;1. The blood plasma profiles collected at various timepoints (day 1, day 13, or day 25) were compared to samples collected pre-irradiation and immediately prior to euthanasia (preterminal). Both independent (unpaired) and dependent (paired) statistical tests were performed, and these results can be viewed in Supplementary Tables\u0026nbsp;2 and 3. Additionally, Supplementary Table\u0026nbsp;4 combines all comparisons for a holistic view across all groups and time points. For nonparametric data analysis, Mann-Whitney U tests were performed for unpaired comparisons, while for paired comparisons, the Wilcoxon signed-rank test was utilized. A p-value of less than 0.05 was considered statistically significant. Additionally, in an effort to address the issue of multiple comparisons that can potentially increase the likelihood of false positives, a False Discovery Rate (FDR) method was applied to adjust p-values. A more detailed summary of the statistical analyses used to analyze this data has been discussed in a recently published paper \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the staff of the Radiation Science Department for dosimetry and radiation exposure to the animals, and to the staff of Veterinary Science Department for animal care. The authors would like to acknowledge the Mass Spectrometry and Analytical Pharmacology Shared Resource in Georgetown University (Washington, DC, USA) partially supported by NIH/NCI/CCSG grant P30-CA051008. We are thankful to Folade Olabisi for assistance in revising manuscript content. 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eStudy design: VKS; Performance of the study: OOF, SYW, VKS; Data acquisition, curation and analysis: VKS, AKC, ADC, OOF, SYW, JBT; Drafting of the manuscript: ADC, VKS, AKC, OOF, SYW; Revision of manuscript content: VKS, AKC, ADC, OOF, SYW; Supervision: VKS; Funding acquisition: VKS.\u0026nbsp;All authors have read and approved the final submitted manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe authors gratefully acknowledge the research support from the Uniformed Services University of the Health Sciences/Armed Forces Radiobiology Research Institute (grant # AFR-B4-10978 and 12080) to VKS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by The Institutional Animal Care and Use Committee - Armed Forces Radiobiology Research Institute Approval Code: 2015-12-010, Approval Date: February 24, 2016. Department of Defense second tier approval: Department of Defense Animal Care and Use Review Office (ACURO) Approval Code: 2015-12-010, Approval Date: March 02, 2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll relevant data are within the manuscript and its Supporting Information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eJohn B. Tyburski is employee of Nelson Scientific Labs, LLC, Potomac. The paper reflects the views of the scientists, and not the company. Other authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGale, R. P., Armitage, J. O. \u0026amp; Hashmi, S. K. Emergency response to radiological and nuclear accidents and incidents. \u003cem\u003eBritish journal of haematology\u003c/em\u003e \u003cstrong\u003e192\u003c/strong\u003e, 968-972, doi:10.1111/bjh.16138 (2021).\u003c/li\u003e\n\u003cli\u003eFliedner, T. M.\u003cem\u003e et al.\u003c/em\u003e Radiologic and nuclear events: the METREPOL severity of effect grading system. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 5757-5758; author reply 5758-5759, doi:10.1182/blood-2008-04-150243 (2008).\u003c/li\u003e\n\u003cli\u003eWeinstock, D. M.\u003cem\u003e et al.\u003c/em\u003e Radiologic and nuclear events: contingency planning for hematologists/oncologists. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 5440-5445, doi:10.1182/blood-2008-01-134817 (2008).\u003c/li\u003e\n\u003cli\u003eSingh, V. K., Newman, V. L., Romaine, P. L., Hauer-Jensen, M. \u0026amp; Pollard, H. B. Use of biomarkers for assessing radiation injury and efficacy of countermeasures. \u003cem\u003eExpert review of molecular diagnostics\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 65-81, doi:10.1586/14737159.2016.1121102 (2016).\u003c/li\u003e\n\u003cli\u003eGale, R. P. \u0026amp; Armitage, J. O. Are we prepared for nuclear terrorism? \u003cem\u003eThe New England journal of medicine\u003c/em\u003e \u003cstrong\u003e378\u003c/strong\u003e, 1246-1254, doi:10.1056/NEJMsr1714289 (2018).\u003c/li\u003e\n\u003cli\u003eDonnelly, E. H.\u003cem\u003e et al.\u003c/em\u003e Acute radiation syndrome: assessment and management. \u003cem\u003eSouthern medical journal\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 541-546, doi:10.1097/SMJ.0b013e3181ddd571 (2010).\u003c/li\u003e\n\u003cli\u003eSingh, V. K. \u0026amp; Seed, T. M. in \u003cem\u003ePhysician\u0026apos;s Guide to Terrorist Attack\u003c/em\u003e (ed Michael J Roy) Ch. 24, 339-362 (Humana Press, 2003).\u003c/li\u003e\n\u003cli\u003eAryankalayil, M.\u003cem\u003e et al.\u003c/em\u003e Biomarkers for Biodosimetry and Their Role in Predicting Radiation Injury. \u003cem\u003eCytogenetic and genome research\u003c/em\u003e \u003cstrong\u003e163\u003c/strong\u003e, 103-109, doi:10.1159/000531444 (2023).\u003c/li\u003e\n\u003cli\u003eRana, S., Kumar, R., Sultana, S. \u0026amp; Sharma, R. K. Radiation-induced biomarkers for the detection and assessment of absorbed radiation doses. \u003cem\u003eJournal of pharmacy \u0026amp; bioallied sciences\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 189-196, doi:10.4103/0975-7406.68500 (2010).\u003c/li\u003e\n\u003cli\u003eWinters, T. A., Taliaferro, L. P. \u0026amp; Satyamitra, M. M. Development of Biomarkers for Radiation Biodosimetry and Medical Countermeasures Research: Current Status, Utility, and Regulatory Pathways. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e197\u003c/strong\u003e, 554-558, doi:10.1667/rade-21-00213.1 (2022).\u003c/li\u003e\n\u003cli\u003eCaliff, R. M. Biomarker definitions and their applications. \u003cem\u003eExperimental biology and medicine (Maywood, N.J\u003c/em\u003e \u003cstrong\u003e243\u003c/strong\u003e, 213-221, doi:10.1177/1535370217750088 (2018).\u003c/li\u003e\n\u003cli\u003eManna, S. K., Krausz, K. W., Bonzo, J. A., Idle, J. R. \u0026amp; Gonzalez, F. J. Metabolomics reveals aging-associated attenuation of noninvasive radiation biomarkers in mice: potential role of polyamine catabolism and incoherent DNA damage-repair. \u003cem\u003eJournal of proteome research\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 2269-2281, doi:10.1021/pr400161k (2013).\u003c/li\u003e\n\u003cli\u003eChen, Z.\u003cem\u003e et al.\u003c/em\u003e Rapid and high-throughput detection and quantitation of radiation biomarkers in human and nonhuman primates by differential mobility spectrometry-mass spectrometry. \u003cem\u003eJournal of the American Society for Mass Spectrometry\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 1626-1636, doi:10.1007/s13361-016-1438-5 (2016).\u003c/li\u003e\n\u003cli\u003eRutten, E. A. \u0026amp; Badie, C. Radiation biomarkers: Silver bullet, or wild goose chase? \u003cem\u003eJ Pers Med\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, doi:10.3390/jpm11070603 (2021).\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. Table of pharmacogenomic biomarkers in drug labeling. 2015. Available at: http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm [Last accessed October 25, 2015]\u003c/li\u003e\n\u003cli\u003eEuropean Medicines Agency. Qualification of novel methodologies for medicine development. 2015. Available at: http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp\u0026amp;mid=WC0b01ac0580022bb0 [Last accessed October 25, 2015]\u003c/li\u003e\n\u003cli\u003ePharmaceutical and Medical Devices Agency. Record of consultations on pharmacogenomics/biomarkers. 2010. Available at: https://www.pmda.go.jp/english/review-services/consultations/0001.html [Last accessed October 25, 2015]\u003c/li\u003e\n\u003cli\u003eChaudhry, M. A. Biomarkers for human radiation exposure. \u003cem\u003eJ Biomed Sci\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 557-563, doi:10.1007/s11373-008-9253-z (2008).\u003c/li\u003e\n\u003cli\u003eKang, C. M.\u003cem\u003e et al.\u003c/em\u003e Possible biomarkers for ionizing radiation exposure in human peripheral blood lymphocytes. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 312-319 (2003).\u003c/li\u003e\n\u003cli\u003eAmundson, S. A.\u003cem\u003e et al.\u003c/em\u003e Identification of potential mRNA biomarkers in peripheral blood lymphocytes for human exposure to ionizing radiation. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e154\u003c/strong\u003e, 342-346 (2000).\u003c/li\u003e\n\u003cli\u003eCarpenter, A. D.\u003cem\u003e et al.\u003c/em\u003e Metabolomic changes in plasma of preterminal stage of rhesus nonhuman primates exposed to lethal dose of radiation. \u003cem\u003eMetabolites\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 18, doi:10.3390/metabo14010018 (2024).\u003c/li\u003e\n\u003cli\u003eSchule, S.\u003cem\u003e et al.\u003c/em\u003e Gene Expression Changes in a Prefinal Health Stage of Lethally Irradiated Male and Female Rhesus Macaques. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e199\u003c/strong\u003e, 17-24, doi:10.1667/RADE-22-00083.1 (2023).\u003c/li\u003e\n\u003cli\u003eSingh, V. K., Srivastava, M. \u0026amp; Seed, T. M. Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions. \u003cem\u003eExpert Rev Proteomics\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 221-246, doi:10.1080/14789450.2023.2263652 (2023).\u003c/li\u003e\n\u003cli\u003eGirgis, M.\u003cem\u003e et al.\u003c/em\u003e Comparative proteomic analysis of serum from nonhuman primates administered BIO 300: a promising radiation countermeasure. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 19343, doi:10.1038/s41598-020-76494-4 (2020).\u003c/li\u003e\n\u003cli\u003eCarpenter, A. D.\u003cem\u003e et al.\u003c/em\u003e Analysis of the proteomic profile in serum of irradiated nonhuman primates treated with Ex-Rad, a radiation medical countermeasure. \u003cem\u003eJournal of proteome research\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1116-1126, doi:10.1021/acs.jproteome.2c00458 (2023).\u003c/li\u003e\n\u003cli\u003eRosen, E., Fatanmi, O. O., Wise, S. Y., Rao, V. A. \u0026amp; Singh, V. K. Gamma-tocotrienol, a radiation countermeasure, reverses proteomic changes in serum following total-body gamma irradiation in mice. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3387, doi:10.1038/s41598-022-07266-5 (2022).\u003c/li\u003e\n\u003cli\u003eRosen, E., Fatanmi, O. O., Wise, S. Y., Rao, V. A. \u0026amp; Singh, V. K. Tocol prophylaxis for total-body irradiation: A proteomic analysis in murine model. \u003cem\u003eHealth Phys\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, 12-20, doi:10.1097/HP.0000000000001221 (2020).\u003c/li\u003e\n\u003cli\u003eSrivastava, A.\u003cem\u003e et al.\u003c/em\u003e Personalized Radioproteomics: Identification of a Protein Biomarker Signature for Preemptive Rescue by Tocopherol Succinate in CD34(+) Irradiated Progenitor Cells Isolated from a Healthy Control Donor. \u003cem\u003eJ Proteomics Bioinform\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 23-30, doi:10.4172/jpb.1000349 (2015).\u003c/li\u003e\n\u003cli\u003eMacintyre, A. N.\u003cem\u003e et al.\u003c/em\u003e Long-term recovery of the adaptive immune system in rhesus macaques after total body irradiation. \u003cem\u003eAdvances in radiation oncology\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 100677, doi:10.1016/j.adro.2021.100677 (2021).\u003c/li\u003e\n\u003cli\u003eKamiya, K.\u003cem\u003e et al.\u003c/em\u003e Long-term effects of radiation exposure on health. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e386\u003c/strong\u003e, 469-478, doi:10.1016/s0140-6736(15)61167-9 (2015).\u003c/li\u003e\n\u003cli\u003eCohen\u0026ndash;Jonathan, E., Bernhard, E. J. \u0026amp; McKenna, W. G. How does radiation kill cells? \u003cem\u003eCurr. Opin. Chem. Biol.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 77-83, doi:https://doi.org/10.1016/S1367-5931(99)80014-3 (1999).\u003c/li\u003e\n\u003cli\u003eZhao, X., Guo, Y., Li, L. \u0026amp; Li, Y. Longitudinal change of serum inter-alpha-trypsin inhibitor heavy chain H4, and its correlation with inflammation, multiorgan injury, and death risk in sepsis. \u003cem\u003eJ. Clin. Lab. Anal.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, e24834, doi:10.1002/jcla.24834 (2023).\u003c/li\u003e\n\u003cli\u003eKashyap, R. S.\u003cem\u003e et al.\u003c/em\u003e Inter-alpha-trypsin inhibitor heavy chain 4 is a novel marker of acute ischemic stroke. \u003cem\u003eClin. Chim. Acta\u003c/em\u003e \u003cstrong\u003e402\u003c/strong\u003e, 160-163, doi:10.1016/j.cca.2009.01.009 (2009).\u003c/li\u003e\n\u003cli\u003eNational Research Council of the National Academy of Sciences. \u003cem\u003eGuide for the care and use of laboratory animals\u003c/em\u003e. 8th edn, (National Academies Press, 2011).\u003c/li\u003e\n\u003cli\u003eSingh, V. K., Fatanmi, O. O., Wise, S. Y., Carpenter, A. D. \u0026amp; Olsen, C. H. Determination of lethality curve for cobalt-60 gamma-radiation source in rhesus macaques using subject-based supportive care. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e198\u003c/strong\u003e, 599-614, doi:10.1667/RADE-22-00101.1 (2022).\u003c/li\u003e\n\u003cli\u003ePhipps, A. J., Bergmann, J. N., Albrecht, M. T., Singh, V. K. \u0026amp; Homer, M. J. Model for evaluating antimicrobial therapy to prevent life-threatening bacterial infections following exposure to a medically significant radiation dose. \u003cem\u003eAntimicrobial agents and chemotherapy\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, e0054622, doi:10.1128/aac.00546-22 (2022).\u003c/li\u003e\n\u003cli\u003eLi, Y.\u003cem\u003e et al.\u003c/em\u003e Transcriptome of rhesus macaque (\u003cem\u003eMacaca mulatta\u003c/em\u003e) exposed to total-body irradiation. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 6295, doi:10.1038/s41598-021-85669-6 (2021).\u003c/li\u003e\n\u003cli\u003eLi, Y.\u003cem\u003e et al.\u003c/em\u003e Analysis of the metabolomic profile in serum of irradiated nonhuman primates treated with Ex-Rad, a radiation countermeasure. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 11449, doi:10.1038/s41598-021-91067-9 (2021).\u003c/li\u003e\n\u003cli\u003eSingh, V. K.\u003cem\u003e et al.\u003c/em\u003e Radioprotective efficacy of gamma-tocotrienol in nonhuman primates. \u003cem\u003eRadiat Res\u003c/em\u003e \u003cstrong\u003e185\u003c/strong\u003e, 285-298, doi:10.1667/RR14127.1 (2016).\u003c/li\u003e\n\u003cli\u003eInternational Standardization Organization and ASTM International. in \u003cem\u003eStandard Practice for Use of an Alanine-EPR Dosimetry System.\u003c/em\u003e 7 (ASTM International, ISO and West Conshohocken (US:PA):).\u003c/li\u003e\n\u003cli\u003eNagy, V. Accuracy considerations in EPR dosimetry. \u003cem\u003eAppl Radiat Isot\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1039-1050 (2000).\u003c/li\u003e\n\u003cli\u003eCheema, A. K.\u003cem\u003e et al.\u003c/em\u003e Identification of novel biomarkers for acute radiation injury using multiomics approach and nonhuman primate model. \u003cem\u003eInt J Radiat Oncol Biol Phys\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 310-320, doi:10.1016/j.ijrobp.2022.05.046 (2022).\u003c/li\u003e\n\u003cli\u003eAmerican Veterinary Medical Association. AVMA Guidelines for the Euthanasia of Animals: 2020 Edition. 2020. Available at: https://www.avma.org/sites/default/files/2020-01/2020-Euthanasia-Final-1-17-20.pdf [Last accessed December 29, 2022]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biomarkers, gamma-radiation, nonhuman primates, preterminal, proteomics, total-body irradiation","lastPublishedDoi":"10.21203/rs.3.rs-4190029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4190029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe identification and validation of radiation biomarkers is critical for assessing the radiation dose received in exposed individuals and for developing radiation medical countermeasures that can be used to treat acute radiation syndrome (ARS). Additionally, a fundamental understanding of the effects of radiation injury could further aid in the identification and development of therapeutic targets for mitigating radiation damage. In this study, blood samples were collected from fourteen male nonhuman primates (NHPs) that were exposed to 7.2 Gy ionizing radiation at various time points (seven days prior to irradiation; 1, 13, and 25 days post-irradiation; as well as immediately prior to the euthanasia of moribund animals (preterminal)). Plasma was isolated from these samples and was analyzed using a liquid chromatography tandem mass spectrometry approach in an effort to determine the effects of radiation on plasma proteomic profiles. Of particular interest was to determine if the expression of certain proteins reacted to radiation in a way that would act as a predictor for health decline leading to a preterminal phenotype. Our results suggest that radiation induced a diverse temporal pattern among protein expression that displayed prominent changes within NHP proteomic plasma profiles. Of these significantly altered proteins, several play important roles in certain biological processes such as hemostasis, inflammation, and immune response.\u003c/p\u003e","manuscriptTitle":"Proteomic Analysis of Plasma at the Preterminal Stage of Rhesus Nonhuman Primates Exposed to a Lethal Total-Body Dose of Gamma-Radiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 16:15:05","doi":"10.21203/rs.3.rs-4190029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-25T06:56:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-23T09:10:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-22T18:29:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0cb8e274-782f-4c25-b44d-6bf4d1f16a62","date":"2024-04-11T06:30:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11e1868e-bd00-439d-906b-e01da9e33490","date":"2024-04-06T08:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-05T14:07:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-05T13:59:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-05T11:17:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-05T11:13:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-30T00:10:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"371f0622-be33-4648-b51e-ff8fb7521755","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:55:32+00:00","versionOfRecord":{"articleIdentity":"rs-4190029","link":"https://doi.org/10.1038/s41598-024-64316-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-06-12 14:55:32","publishedOnDateReadable":"June 12th, 2024"},"versionCreatedAt":"2024-04-10 16:15:05","video":"","vorDoi":"10.1038/s41598-024-64316-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-64316-w","workflowStages":[]},"version":"v1","identity":"rs-4190029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4190029","identity":"rs-4190029","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}