DOI: 10.5061/dryad.9ghx3ffx0
title: Serum proteomic signatures in non-human primates following treatment with a radiation countermeasure and exposure to a partial- or total-body supralethal radiation dose

description: 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.
description: # Serum proteomic signatures in non-human primates following treatment
 with a radiation countermeasure and exposure to a partial- or total-body
 supralethal radiation dose Dataset DOI:
 [10.5061/dryad.9ghx3ffx0](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.