Serum proteomic signatures in non-human primates following treatment with a radiation countermeasure and exposure to a partial- or total-body supralethal radiation dose
Data files
Jan 22, 2026 version files 288.90 GB
-
AC1636_DIA_mergedSL_210samples_peptide_report210.csv
26.21 MB
-
AC1636_DIA_mergedSL_210samples_protein_report.csv
1.66 MB
-
DDA_library.zip
33.64 GB
-
DIA-selected_1.zip
44.67 GB
-
DIA-selected_2.zip
45.87 GB
-
DIA-selected_3.zip
48.84 GB
-
DIA-selected_4.zip
43.69 GB
-
DIA-selected_5.zip
36.29 GB
-
DIA-selected_6.zip
35.86 GB
-
README.md
7.81 KB
Abstract
As countries face emerging conflicts and evaluate military strategies, the potential for nuclear-related accidents continues to rise, putting large populations of civilians at risk. As a result, the development of pharmaceuticals that can be administered prior to radiation exposure to protect from radiation-induced injury is of utmost importance. However, there are currently no prophylactic drugs that can be used to protect against radiation injury. One drug under advanced development, gamma-tocotrienol (GT3), has proved to be promising in terms of its antioxidant activity as well as its accelerated hematopoietic recovery and reduction of DNA damage in treated animals exposed to various doses of ionizing radiation. In this study, nonhuman primates (NHPs) were leveraged to investigate the protective effects of GT3 on proteomic profiles in conjunction with a supralethal dose (12 Gy) of either total-body irradiation (TBI) or partial-body irradiation (PBI), performed with 5% bone marrow sparing. Animals were treated with either GT3 or vehicle 24 h prior to irradiation, and blood samples were collected at various time points pre- and post-exposure to assess changes in serum proteomic profiles. Both PBI and TBI induced significant dysregulation of pathways related to extracellular matrix and organization, hemostasis, and immune response. Notably, administration of GT3 offered significant protection against radiation-induced damage by either partial- or total-body irradiation in these pathways. Overall, this study offers insight into the biochemical mechanisms of the drug, pathways, and proteins adversely affected by radiation, and potential biomarkers that can be further investigated to accurately assess absorbed radiation doses in exposed populations.
Dataset DOI: 10.5061/dryad.9ghx3ffx0
Description of the data and file structure
A total of 32 NHPs (rhesus macaques) were used for this study; 16 animals were exposed to partial-body radiation (LINAC X-ray radiation, 1.3 Gy/min) and the remaining 16 animals were exposed to total-body radiation (cobalt-60 γ-radiation, 0.6 Gy/min). In each group, eight animals were administered either vehicle or 37.5 mg/kg of GT3 subcutaneously (sc) 24 h prior to irradiation. Blood samples were collected from NHPs undergoing partial- or total-body irradiation (PBI or TBI) at the following time points: 3 d prior to irradiation and 4, 8, 12 h, 1, 2, and 6 d post-irradiation. Serum was isolated and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a data-independent acquisition (DIA) workflow.
Files and variables
File: AC1636_DIA_mergedSL_210samples_protein_report.csv
Description: Normalized protein abundances. Columns 1:4 provide information on protein identifications (UniProt IDs), while the remaining columns are sample-level protein abundances that can be matched to the DIA files (.d format).
Each row represents a detected peptide and its quantitative signal across 210 DIA samples.
It contains peptide sequence, charge, protein mapping, and confidence/quality information.
Sample-specific columns report peptide intensities; “Filtered” means the peptide was not reliably quantified in that sample.
Variables
- PG.ProteinGroups
- PG.Genes
- PG.ProteinDescriptions
- PG.ProteinNames
- [1] t220127_AC1636_DIA_01_S1-A1_1_1334.d.PG.Quantity
- [2] t220127_AC1636_DIA_02_S1-A2_1_1335.d.PG.Quantity
- [3] t220127_AC1636_DIA_03_S1-A3_1_1336.d.PG.Quantity
- [4] t220127_AC1636_DIA_04_S1-A4_1_1337.d.PG.Quantity
- [5] t220127_AC1636_DIA_05_S1-A5_1_1338.d.PG.Quantity
- [6] t220127_AC1636_DIA_06_S1-A6_1_1339.d.PG.Quantity
- [7] t220127_AC1636_DIA_07_S1-A7_1_1340.d.PG.Quantity
- [8] t220127_AC1636_DIA_08_S1-A8_1_1341.d.PG.Quantity
- [9] t220127_AC1636_DIA_09_S1-A9_1_1342.d.PG.Quantity
- [10] t220127_AC1636_DIA_100_S2-A4_1_1448.d.PG.Quantity
- [11] t220127_AC1636_DIA_101_S2-A5_1_1449.d.PG.Quantity
- [12] t220127_AC1636_DIA_102_S2-A6_1_1450.d.PG.Quantity
- [13] t220127_AC1636_DIA_103_S2-A7_1_1451.d.PG.Quantity
- [14] t220127_AC1636_DIA_104_S2-A8_1_1452.d.PG.Quantity
- [15] t220127_AC1636_DIA_105_S2-A9_1_1453.d.PG.Quantity
- [16] t220127_AC1636_DIA_106_S2-A10_1_1454.d.PG.Quantity
- [17] t220127_AC1636_DIA_107_S2-A11_1_1455.d.PG.Quantity
- [18] t220127_AC1636_DIA_108_S2-A12_1_1456.d.PG.Quantity
- [19] t220127_AC1636_DIA_109_S2-B1_1_1457.d.PG.Quantity
- [20] t220127_AC1636_DIA_10_S1-A10_1_1343.d.PG.Quantity
- [21] t220127_AC1636_DIA_110_S2-B2_1_1458.d.PG.Quantity
- [22] t220127_AC1636_DIA_111_S2-B3_1_1459.d.PG.Quantity
- [23] t220127_AC1636_DIA_112_S2-B4_1_1460.d.PG.Quantity
- [24] t220127_AC1636_DIA_113_S2-B5_1_1461.d.PG.Quantity
- [25] t220127_AC1636_DIA_114_S2-B6_1_1462.d.PG.Quantity
- [26] t220127_AC1636_DIA_115_S2-B7_1_1463.d.PG.Quantity
- [27] t220127_AC1636_DIA_116_S2-B8_1_1464.d.PG.Quantity
- [28] t220127_AC1636_DIA_117_S2-B9_1_1465.d.PG.Quantity
- [29] t220127_AC1636_DIA_118_S2-B10_1_1466.d.PG.Quantity
- [30] t220127_AC1636_DIA_119_S2-B11_1_1467.d.PG.Quantity
- [31] t220127_AC1636_DIA_11_S1-A11_1_1344.d.PG.Quantity
- [32] t220127_AC1636_DIA_120_S2-B12_1_1468.d.PG.Quantity
- [33] t220127_AC1636_DIA_121_S2-C1_1_1472.d.PG.Quantity
- [34] t220127_AC1636_DIA_122_S2-C2_1_1473.d.PG.Quantity
- [35] t220127_AC1636_DIA_123_S2-C3_1_1474.d.PG.Quantity
- [36] t220127_AC1636_DIA_124_S2-C4_1_1475.d.PG.Quantity
- [37] t220127_AC1
File: AC1636_DIA_mergedSL_210samples_peptide_report210.csv
Description: Peptide level quantification and identification data. Each row corresponds to a peptide precursor, and columns include protein annotations.
Each row represents a protein group with abundances aggregated from its peptides.
It includes protein accessions, gene names, and functional descriptions.
Sample columns give protein-level quantities; “Filtered” indicates unreliable protein quantification in that run.
Variables
- PG.ProteinGroups
- PG.ProteinAccessions
- PG.Genes
- PG.ProteinDescriptions
- PG.UniProtIds
- PG.ProteinNames
- EG.PrecursorId
- [1] t220127_AC1636_DIA_01_S1-A1_1_1334.d.EG.Qvalue
- [2] t220127_AC1636_DIA_02_S1-A2_1_1335.d.EG.Qvalue
- [3] t220127_AC1636_DIA_03_S1-A3_1_1336.d.EG.Qvalue
- [4] t220127_AC1636_DIA_04_S1-A4_1_1337.d.EG.Qvalue
- [5] t220127_AC1636_DIA_05_S1-A5_1_1338.d.EG.Qvalue
- [6] t220127_AC1636_DIA_06_S1-A6_1_1339.d.EG.Qvalue
- [7] t220127_AC1636_DIA_07_S1-A7_1_1340.d.EG.Qvalue
- [8] t220127_AC1636_DIA_08_S1-A8_1_1341.d.EG.Qvalue
- [9] t220127_AC1636_DIA_09_S1-A9_1_1342.d.EG.Qvalue
- [10] t220127_AC1636_DIA_100_S2-A4_1_1448.d.EG.Qvalue
- [11] t220127_AC1636_DIA_101_S2-A5_1_1449.d.EG.Qvalue
- [12] t220127_AC1636_DIA_102_S2-A6_1_1450.d.EG.Qvalue
- [13] t220127_AC1636_DIA_103_S2-A7_1_1451.d.EG.Qvalue
- [14] t220127_AC1636_DIA_104_S2-A8_1_1452.d.EG.Qvalue
- [15] t220127_AC1636_DIA_105_S2-A9_1_1453.d.EG.Qvalue
- [16] t220127_AC1636_DIA_106_S2-A10_1_1454.d.EG.Qvalue
- [17] t220127_AC1636_DIA_107_S2-A11_1_1455.d.EG.Qvalue
- [18] t220127_AC1636_DIA_108_S2-A12_1_1456.d.EG.Qvalue
- [19] t220127_AC1636_DIA_109_S2-B1_1_1457.d.EG.Qvalue
- [20] t220127_AC1636_DIA_10_S1-A10_1_1343.d.EG.Qvalue
- [21] t220127_AC1636_DIA_110_S2-B2_1_1458.d.EG.Qvalue
- [22] t220127_AC1636_DIA_111_S2-B3_1_1459.d.EG.Qvalue
- [23] t220127_AC1636_DIA_112_S2-B4_1_1460.d.EG.Qvalue
- [24] t220127_AC1636_DIA_113_S2-B5_1_1461.d.EG.Qvalue
- [25] t220127_AC1636_DIA_114_S2-B6_1_1462.d.EG.Qvalue
- [26] t220127_AC1636_DIA_115_S2-B7_1_1463.d.EG.Qvalue
- [27] t220127_AC1636_DIA_116_S2-B8_1_1464.d.EG.Qvalue
- [28] t220127_AC1636_DIA_117_S2-B9_1_1465.d.EG.Qvalue
- [29] t220127_AC1636_DIA_118_S2-B10_1_1466.d.EG.Qvalue
- [30] t220127_AC1636_DIA_119_S2-B11_1_1467.d.EG.Qvalue
- [31] t220127_AC1636_DIA_11_S1-A11_1_1344.d.EG.Qvalue
- [32] t220127_AC1636_DIA_120_S2-B12_1_1468.d.EG.Qvalue
- [33] t220127_AC1636_DIA_121_S2-C1_1_1472.d.EG.Qvalue
- [34] t220127_AC1636_DIA_122_S2-C2_1_1473.d.EG.Qvalue
- [35] t220127_AC1636_DIA_123_S2-C3_1_1474.d.EG.Qvalue
- [36] t220127_AC1636_DIA_124_S2-C4_1_1475.d.EG.Qvalue
- [37] t220127_AC1636_DIA_125_S2-C5_1_14
Spectral Library
File: DDA_library.zip
Description: DDA-based spectral library used to support peptide identification in DIA analysis. Includes fragment ion spectra, retention times, and precursor information.
Raw DIA MS Data (1-6)
Description: Raw DIA MS files generated from serum samples. Each .d directory stores MS1 and MS2 spectral information including precursor and fragment ion m/z values, intensities, retention times, and ion mobility separation (when applicable). Each folder has an XML Sample Table file generated by Bruker HyStar (LC–MS control software). It describes how and where a DIA sample was acquired.
File: DIA-selected_1.zip
Description: DIA runs (1/6).
File: DIA-selected_2.zip
Description: DIA runs (2/6).
File: DIA-selected_3.zip
Description: DIA runs (3/6).
File: DIA-selected_4.zip
Description: DIA runs (4/6).
File: DIA-selected_6.zip
Description: DIA runs (6/6).
File: DIA-selected_5.zip
Description: DIA runs (5/6).
Code/software
Microsoft Excel is needed to view the protein and peptide reports (.csv). Raw mass spectrometry files (.d) can be viewed with Bruker, Spectronaut, or another compatible software like OpenMS.
