Liquid ChromatographyâMass Spectrometry-Based Metabolomics of

Subscriber access provided by UNIV OF TEXAS DALLAS

Article

Liquid Chromatography – Mass Spectrometry based Metabolomics of Nonhuman Primates after 4 Gy Total Body Radiation Exposure: Global Effects and Targeted Panels Evan L Pannkuk, Evagelia C. Laiakis, Kirandeep Gill, Shreyans K Jain, Khyati Y Mehta, Denise Nishita, Kim Bujold, James Bakke, Janet Gahagen, Simon Authier, Polly Chang, and Albert J. Fornace Jr. J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00101 • Publication Date (Web): 07 Mar 2019 Downloaded from http://pubs.acs.org on March 16, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Liquid Chromatography – Mass Spectrometry based Metabolomics of Nonhuman Primates after 4 Gy Total Body Radiation Exposure: Global Effects and Targeted Panels

Evan L. Pannkuka, Evagelia C. Laiakisa,b, Kirandeep Gillb, Shreyans K. Jainb, Khyati Y. Mehtaa, Denise Nishitac, Kim Bujoldd, James Bakkec, Janet Gahagenc, Simon Authierd, Polly Changc, and Albert J. Fornace Jr.a,b*

a Department

of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University

Medical Center, Washington, DC, United States of America b Department

of Biochemistry and Molecular & Cellular Biology, Georgetown University

Medical Center, Washington, DC, United States of America c

SRI International, Menlo Park, CA, United States of America

d Citoxlab

North America, Laval, Canada

Corresponding Author

*Albert J. Fornace Jr. Georgetown University, 3970 Reservoir Road, NW, New Research Building, Room E504, Washington, DC 20057 E-mail: [email protected], Phone: (202) 687-7843

1

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 33

Abstract Rapid assessment of radiation signatures in noninvasive biofluids may aid in assigning proper medical treatments for acute radiation syndrome (ARS) and delegating limited resources after a nuclear disaster. Metabolomic platforms allow for rapid screening of biofluid signatures and show promise in differentiating radiation quality and time post-exposure. Here, we use global metabolomics to differentiate temporal effects (1 – 60 d) found in nonhuman primate (NHP) urine and serum small molecule signatures after a 4 Gy total body irradiation. Random Forests analysis differentially classifies biofluid signatures according to days post 4 Gy exposure. Eight compounds involved in protein metabolism, fatty acid β oxidation, DNA base deamination, and general energy metabolism were identified in each urine and serum sample, and validated through tandem MS. The greatest perturbations were seen at 1 d in urine and 1 to 21 d in serum. Furthermore, we developed a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) with multiple reaction monitoring (MRM) method to quantify a six compound panel (hypoxanthine, carnitine, acetylcarnitine, proline, taurine, and citrulline) identified in a previous training cohort at 7 d after a 4 Gy exposure. The highest sensitivity and specificity for classifying exposure at 7d after a 4 Gy exposure included carnitine and acetylcarnitine in urine and for taurine, carnitine, and hypoxanthine for serum. Receiver operator characteristic (ROC) curve analysis using combined compounds show excellent sensitivity and specificity in urine (area under the curve [AUC] = 0.99) and serum (AUC = 0.95). These results highlight the utility of MS platforms to differentiate time post-exposure and acquire reliable quantitative biomarker panels for classifying exposed individuals. Keywords: Targeted Metabolomics, Ionizing Radiation, Nonhuman Primates, Biodosimetry

2


Page 3 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1. Introduction Due to threats ranging from malicious use of radioactive materials against the public and increasing presence from industrial waste (e.g., nuclear power plants, medical devices), several concerted efforts from international entities have focused on radiological countermeasures and recovery, such as radiation biodosimetry and mitigation (National Institute of Allergy and Infectious Diseases [NIAID]), standardized medical treatment protocols (METREPOL) (European Atomic Energy Community [EAEC]), among others to minimize damage from large scale radiation exposures.1–3 Acute radiation syndrome (ARS) describes a plethora of symptoms after total body irradiation (TBI) or a significant partial body irradiation at doses > 0.5 Gy.4 Symptoms manifest as a dynamic interplay from multi-organ damage, the most critical arising from dysfunction to hematopoietic, gastrointestinal (GI), neurovascular, and cutaneous organ systems.5, 6 Mild levels of radiation exposure (< 2 Gy) would require minor if any medical treatment (vomiting, headache, and fever would be present in some) as a full recovery would be expected (unless exacerbated by combined injury or chronic illness). Higher exposure levels (2 – 4 Gy) exhibit increased severity of the above symptoms and onset of hematopoietic syndrome and the associated lymphopenia, thrombocytopenia, and granulopenia.7 This exposure range poses an important point of ARS as possible mortality could be prevented with proper supportive care (i.e., cytokine therapy) and high throughput dose estimates could aid in defining medical treatments. Another critical parameter is time of assessment after exposure since casualties will probably arrive for triage and assessment at varying times after exposures. To address this issue, the NIAID Centers for Medical Countermeasures Against Radiation (CMCR) program chose a sublethal TBI dose which would allow the entire cohort to be assessed over a 60 d time span.

3



Page 4 of 33

Currently, the lack of rapid and reliable dose and time estimates (including exposure distribution and rate) leaves trained medical staff to rely solely on symptoms as an indicator of ARS.1 Recent efforts have explored the use of metabolomics (collective analysis of biomolecules < 1 kDa) to provide quicker dose estimates of ARS by measuring perturbed metabolite levels in biofluids.8 Ionizing radiation can damage tissues and cellular targets by directly interacting with biomolecules or through free radical formation. The sum effect of this damage can produce small molecule “signatures” representative of ARS severity. For dose exposures in the 2 – 4 Gy range, metabolomic signatures have been more easily characterized in murine models (mice and rats) for various exposure types and time points (including external Xray, 60Co, and 137Cs exposures and internal emitters [137CsCl, 85/90SrCl2]). However, little has been explored for nonhuman primates (NHPs) and is limited to urine (12 – 72 h, 1 and 3.5 Gy; 60Co

exposure) or 7 d samples (2 and 4 Gy; 137Cs exposure).8, 9 While work on murine models is

critical for initial discovery phase experiments, NHP models are paramount for comparison to human populations.10, 11 Additionally, validation cohorts are required to assess repeatability and clinical utility. In this study, we determined temporal (1 – 60 d) perturbed metabolite levels in NHP urine and serum using a liquid chromatography mass spectrometry (LC-MS) global metabolomics approach after 4 Gy TBI. We found a number of perturbed metabolites and were able to differentiate time points through multivariate analysis. In addition, we selected a panel of metabolites that were significantly altered in previous studies at 7 d post 4 Gy TBI12–14 and developed a rapid LC-MS/MS with multiple reaction monitoring (MRM) assay for simultaneous absolute quantification in biofluids and applied to both the previous and current cohort. While sample sizes are modest, here we refer to a previous cohort12 (n=12) as the training cohort and 4


Page 5 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the current cohort (n=8) as the validation cohort. A combination of three compounds (serum) and two compounds (urine) had excellent sensitivity and specificity for classifying 7 d post 4 Gy exposure. These results further highlight the dynamic state of metabolite flux after irradiation and the importance of timing for biodosimetry. Also, while NHP studies have remained “prohibitively” costly for designs with large sample sizes, validation of potential markers from previous studies is an absolute necessity for ultimate clinical use. 2. Materials and Methods 2.1. Chemicals All reagents were LC-MS optima grade (Fisher Scientific, Hanover Park, IL, USA) and all standards were of the highest purity available. Standards were acquired from Sigma-Aldrich (St. Louis, MO, USA) (hypoxanthine, L-carnitine, acetyl-L-carnitine, propionyl-L-carnitine, isobutyryl-L-carnitine, 2-methylbutyryl-L-carnitine, 7-methylguanine, creatine, xanthine, asymmetric dimethylarginine [ADMA], guanine, oleamide, L-citrulline, L-proline, taurine, 4nitrobenzoic acid, and debrisoquine sulfate), CDN isotopes (Pointe-Claire, QC, Canada) (carnitine-d3, acetyl-d3-carnitine, citrulline-d7, and proline-d3), and Cambridge Isotope Laboratories, Inc. (Andover, MA, USA) (hypoxanthine-d3 and taurine-13C2). 2.2. Nonhuman Primate System Rhesus monkeys (Macaca mulatta) for the validation cohort were irradiated at the same AAALAC accredited facility as the previous training cohort12, 13 and included 8 individuals (half male, half female, avg. 6.0 yrs. and 8.3 kg). NHPs were provided with commercial chow (Teklad certified hi-fiber primate diet #7195C, Envigo, Madison, WI, USA) twice daily, and fresh fruits, vegetables, or juice were intermittently provided. Animals were housed in an environment-controlled facility (temperature, 21°C ± 3°C; relative humidity, 50% ± 20%; 12 hr 5



Page 6 of 33

light/dark cycles) individually for at least 7 d post-exposure then healthy animals were paired or grouped housed. Food was removed the night before TBI, where NHPs were acclimated to the radiation facility and exposed to a total dose of 4 Gy at 0.6 Gy/min with a 60Co γ source over the course of seven minutes (Theratron 1000, Best™ Theratronics, Ottawa, ON, CA) (½ exposure antero-posterior position, ½ exposure postero-anterior position). For this particular animal model, the LD50/60 is ~6.7 Gy and at 4 Gy severe weakness and recumbency is observed in ~40% of individuals if anti-emetics are withheld. Radiation exposure was confirmed with a Farmer® ionization chamber (PTW, Brooklyn, NY, USA) (primary) and two nanoDot™ dosimeters (Landauer® Inc., Glenwood, IL, USA) (secondary, placed midplane of xiphoid and corresponding dorsal area). Animals received ondansetron (2 mg/ml, 1.5 mg/kg, IM) before (45 – 90 min) and after (30 – 45 min) irradiation for emesis. Clinical signs were recorded daily after irradiation and detailed examinations were performed at 1, 3, 5, and 7 d and then weekly. Preexposure samples were collected at -8 and -3 d and post-exposure samples collected at 1, 3, 5, 7, 15, 21, 28, and 60 d. Samples were frozen (-80°C) and shipped to Georgetown University Medical Center (GUMC) at the end of the experiment. The study animals, experimental treatment, and biofluid collection for the previous training cohort have already been described.12– 15

Briefly, NHPs were exposed to a single TBI of 4 Gy (exposure dose rate: 0.6 Gy/min-60Co γ

source), urine and serum were collected at 7 d, and shipped to GUMC. Additional biofluids were collected from a separate unirradiated cohort and used as a control. All animal handling procedures were approved by the Institutional Animal Care and Use Committee. 2.3. Sample preparation and LC-MS instrumentation For global metabolic profiling, urine (20 μl) was deproteinated with 80 μl 50% cold acetonitrile with internal standards (2 μM debrisoquine, 176.1188 [M+H]+; 30 μM 46


Page 7 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


nitrobenzoic acid, 166.0140 [M-H]-), vortexed, placed on ice for 10 min, and centrifuged for 10 min (10,000 x g, 4°C).12 Serum (5 μl) was deproteinated with 195 μl 66% cold acetonitrile and prepared as above.13 Samples were injected (2 μl) and analyzed by ultra performance (UP) LC (column- Acquity BEH C18 1.7 μm, 2.1 x 50 mm [40°C urine, 60°C serum]) coupled to a Xevo® G2 quadrupole time-of-flight (QTOF) MS (Waters, Milford, MA, USA). Both negative and positive electrospray ionization (ESI) data independent acquisition modes were run with leucine enkephalin (556.2771 [M+H]+ or 554.2615 [M-H]-) used as LockSpray™. Compounds of interest were validated through tandem MS with a 5 – 50 V ramping collision energy and comparing fragmentation patterns and m/z to pure standards or online databases if standards were unavailable.16, 17 Mobile phases consisted of water/0.1% formic acid (solvent A), acetonitrile/0.1% formic acid (solvent B), and isopropanol/acetonitrile (90:10)/0.1% formic acid (solvent C). For urine, the flow rate was 0.5 ml/min with a gradient of (solvent A and B) 4.0 min 5% B, 4.0 min 20% B, 5.1 min 95% B, and 1.9 min 5% B. Blanks and quality control (QC) samples were run after every 10 samples. For serum, the flow rate was 0.5 ml/min with a gradient of (solvent A, B, and C) 4.0 min 98:2 A:B, 4.0 min 40:60 A:B, 1.5 min 2:98 A:B, 2.0 min 2:98 A:C, 0.5 min 50:50 A:C, and 1.0 min 98:2 A:B. Blanks were run after every 5 samples and QC samples run after every 10. For targeted profiling, serum (5 μl) was diluted with 200 μl cold methanol:acetonitrile:water (80:15:5) spiked with internal standards (1.25 μg/ml proline-d3; 2.5 μg/ml carnitine-d3, acetyl-d3-carnitine, citrulline-d7, hypoxanthine-d3, and taurine-13C2), vortexed, placed on ice for 10 min, and centrifuged for 10 min (10,000 x g, 4°C). Calibration curves were prepared from 0.001 – 5000 ng/ml of standards spiked into charcoal stripped pooled human serum (Innovative Research™, Novi, MI, USA) and QCs were prepared at 3 7



Page 8 of 33

concentrations 5 (low), 50 (medium), and 500 (high) ng/ml. Urine (10 μl) was prepared as above with calibration curves prepared in both solvent and pooled urine. Urine was diluted with an additional 600 μl solvent for quantification of carnitine and taurine. Blanks, internal standards, QCs, samples, and calibration curves were injected (5 μl) with samples into a Waters ACQUITY UPLC coupled to a Xevo® tandem quadrupole (TQ-S) MS operating in MRM mode (columnAcquity BEH amide 1.7 μm 2.1 x 100 mm) maintained at 30°C. MRM transitions were obtained using the “IntelliStart” feature in MassLynx (Table 1) and limit of detection (LOD), limit of quantification (LOQ), range, and linearity of all compounds assessed (Table S1). Mobile phase consisted of water with 10 mM ammonium formate/0.2% formic acid (solvent A) and acetonitrile with 10 mM ammonium formate/0.2% formic acid (solvent B). The flow rate was 0.4 ml/min with a gradient of 2.1 min 100% B, 2.9 min 94% B, 1 min 82% B, 0.1 min 70% B, and 1.4 min 100% B. 2.4. Statistical Analysis The global LC-MS analysis data was processed as previously described.13 After manual inspection in MassLynx v4.1, deconvolution and peak alignment was performed in Progenesis QI (Nonlinear Dynamics, Newcastle, UK) and the raw data file was normalized to the internal standard (debrisoquine [M+H]+, 4-nitrobenzoic acid [M-H]-), and analyzed by unequal variances t-tests (Welch’s t-test, spectral features present ≥ 70%) or presence-absence models (Barnard’s test, spectral features present < 70%) in the software MetaboLyzer.18 The MetaboLyzer data input was normalized as previously described including outlier removal and false discovery rate calculations for multiple testing corrections.13 MetaboLyzer was also used for generating putative identification and pathway analysis (±10 ppm error) of spectral features through the Human Metabolome Database (HMDB), the Kyoto Encyclopedia of Genes and Genomes, 8


Page 9 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


BioCyc, and chemical entities of biological interest (ChEBI).18–23 Pre-exposure samples (-8 and -3 d) were averaged for the control group. The Random Forests (RF) machine learning algorithm through R programming language was used to produce multi-dimensional scaling (MDS) plots and heatmaps.24 Variable importance of metabolites was determined using the RF function and measuring increasing unbiased estimate of the classification error after permutation in MetaboAnalyst.22, 25 For validated compounds, outliers were removed using robust regression and outlier removal and graphed in GraphPad Prism 6.0 (GraphPad Software Inc., La Jolla, CA, USA). For targeted LC-MS/MS analysis, raw data files were processed and analyzed using TargetLynx v4.1. The quantitative data output was then graphed and analyzed by a t-test in GraphPad Prism 6.0 and combined ROC curves were generated in MetaboAnalyst using the linear support vector machine (SVM) model function. 3. Results 3.1. Global LC-MS Metabolomics 3.1.1. Urine A RF algorithm was used to produce a MDS plot and heatmap of the top 100 ranked ions (ESI+) for visualization of the urine metabolomic data matrix (Figure 1). Observation of the MDS plot and heatmap showed the urinary metabolic signature deviates from pre-exposure samples mainly along dimension 2 at 1 d due to increased concentrations of several metabolites (Figure 1). The 3 – 7 d groups were more similar to pre-exposure at 4 Gy, however, better separation of NHPs 7 d post 4 Gy exposure has been observed through RF machine learning when only dose effect is considered.12 A second cluster of spectral features (Figure 1B top) may influence 15 – 28 d groups to separate from pre-exposure samples along dimension 1, again returning at 60 d. Univariate analysis revealed significant increases in carnitine (P < 0.001, 9



Page 10 of 33

208.4 fold change), acetylcarnitine (P < 0.001, 200.4 fold change), propionylcarnitine (P < 0.001, 107.7 fold change), creatine (P < 0.001, 18.5 fold change), 7-methylguanine (P = 0.002, 1.9 fold change), xanthine (P = 0.028, 1.4 fold change), and ADMA (P < 0.001, 2.1 fold change) in urine at 1 d (Table 2 and S2, Figure 2). Isobutyrylcarnitine or butyrylcarnitine also increased at 1 d in urine (P = 0.012, 2.9 fold change), however, we were unable to differentiate between isomers based on fragmentation pattern (represented as isobutyrylcarnitine / butyrylcarnitine). Most metabolites returned closer to basal levels, however, carnitine (3 d [P = 0.004, 27.8 fold change], 5 d [P = 0.023, 14.5 fold change], 7 d [P = 0.012, 11.7 fold change]), acetylcarnitine (7 d [P = 0.005, 8.0 fold change]), propionylcarnitine (3 d [P = 0.015, 11.5 fold change], 7 d [P = 0.024, 6.3 fold change]), and 7-methylguanine (28 d [P = 0.030, 1.5 fold change]) remain slightly higher in urine. Variable importance of validated metabolites was determined with a RF algorithm showing the highest rank for carnitine and acetylcarnitine (Figure S1). 3.1.2. Serum The heatmap produced for the serum data matrix (ESI+) showed general decreases in concentrations for compounds 5 – 60 d post-exposure (Figure S2) and lower concentration variability as the urine data matrix. While metabolite levels remained relatively steady compared to urine, a higher dynamic flux across 60 d post-exposure is observed. Increases were observed in carnitine (1 d [P < 0.001, 1.8 fold change], 3 d [P = 0.004, 1.7 fold change], 5 d [P = 0.015, 1.6 fold change], 7 d [P = 0.045, 1.4 fold change]), and acylcarnitines including isobutyrylcarnitine / butyrylcarnitine (1 d [P = 0.025, 6.4 fold change], 3 d [P = 0.028, 4.1 fold change], 7 d [P = 0.036, 4.9 fold change], 28 d [P = 0.004, 3.3 fold change]), propionylcarnitine (1 d [P = 0.003, 3.0 fold change], 3 d [P = 0.028, 1.3 fold change]), 2-methylbutyroylcarnitine (3 d [P < 0.001, 3.5 fold change], 7 d [P = 0.022, 2.4 fold change], 15 d [P = 0.044, 3.8 fold 10


Page 11 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


change]), and 3-dehydroxycarnitine (1 d [P < 0.001, 1.6 fold change], 3 d [P < 0.001, 1.8 fold change], 5 d [P = 0.017, 1.3 fold change]), (Table 2, Figure 3). Oleamide also slightly increased after 2 weeks (15 d [P = 0.012, 2.5 fold change], 21 d [P = 0.002, 2.2 fold change]). Decreases were observed in hypoxanthine (1 d [P < 0.001, 0.8 fold change], 5 d [P = 0.012, 0.7 fold change], 7 d [P = 0.001, 0.8 fold change], 15 d [P < 0.001, 0.3 fold change], 21 d [P < 0.001, 0.1 fold change], 28 d [P = 0.005, 0.3 fold change], 60 d [P = 0.043, 0.7 fold change]) and guanine (7 d [P = 0.005, 0.5 fold change], 15 d [P < 0.001, 0.1 fold change], 21 d [P < 0.001, 0.1 fold change], 28 d [P = 0.030, 0.5 fold change]). Variable importance of validated metabolites showed the highest rank for guanine followed by hypoxanthine, carnitine / acylcarnitines, and oleamide (Figure S1). 3.2. Targeted LC-MS/MS Metabolomics As previous research has been conducted by our group on NHPs at 7 d post-exposure at multiple doses, we developed a quantitative targeted LC-MS/MS with MRM assay for previously identified significant metabolites (training cohort [n=12]; 7 d post 4 Gy exposure) (Figure S3) and utilized the current cohort (n=8) as a validation set to evaluate classification performance (method validation parameters Table 1 and S1; validated compounds [training cohort] are provided in Table S3).12–15 Biomarker selection was based on metabolites from the training cohort with excellent (citrulline [P = 0.004, AUC = 1.00]), good (carnitine [P = 0.006, AUC = 0.89], acetylcarnitine [P = 0.007, AUC = 0.87]), to fair (taurine [P = 0.068, AUC = 0.75]) sensitivity and specificity in urine (Figure S3). Likewise, serum markers showed excellent (hypoxanthine [P < 0.001, AUC = 0.98], taurine [P < 0.001, AUC = 0.99], citrulline [P = 0.001, AUC = 0.95], proline [P < 0.001, AUC = 0.94]) to fair (carnitine [P = 0.038, AUC = 0.74]) sensitivity and specificity (Figure S3). 11



Page 12 of 33

In the validation cohort, both carnitine (P = 0.022, AUC = 0.80) and acetylcarnitine (P = 0.004, AUC = 0.91) showed significant increases in urine (Figure 4), however, taurine (P = 0.823, AUC = 0.51) and citrulline (P = 0.705, AUC = 0.52) did not significantly change. In serum, carnitine (P = 0.021, AUC = 0.77), hypoxanthine (P < 0.001, AUC = 0.95), and taurine (P = 0.001, AUC = 0.85) significantly changed (Figure 4). Proline (P = 0.397, AUC = 0.57) and citrulline (P = 0.147, AUC = 0.72) slightly decreased but not significantly. AUC values for combined compounds have excellent sensitivity and specificity for both urine (AUC = 0.99) and serum (AUC = 0.95). 4. Discussion NHPs were exposed to 4 Gy TBI (LD50/60 is ~6.7 Gy) and biofluids (urine and serum for the current study) were collected for a 60 d post-exposure duration. A comparable dose in humans would be ~2.2 Gy (LD50/60 is ~4.5 Gy) and would elicit minor hematopoietic syndrome.26, 27 Generally, ARS levels can be classified as mild (< 2Gy), moderate (2 – 4 Gy), severe (4 – 6 Gy), very severe (6 – 8 Gy), to lethal (> 8 Gy) exposures. While milder exposures elicit less severe symptoms (e.g., headache and vomiting), the moderate exposure range may lead to potentially lethal levels that can be ameliorated with supportive care or more active measures.7 We performed global metabolomic analyses for assessment of overall temporal perturbations and found distinct separation of time points by multivariate analysis. Previously, we assessed dose effects (2, 4, 6, 7, and 10 Gy) on NHP urine and serum signatures 7 d post-exposure utilizing LC and GC global profiling12, 13, 15 and demonstrated rapid analysis of a metabolite panel with targeted differential mobility spectrometry (DMS) MS.28 Here, we selected an enriched panel of metabolites and established a targeted quantitative LC-MS/MS with MRM method to determine repeatability in a validation cohort, which is required to ultimately determine their utility in 12


Page 13 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


clinical settings. Of the previously identified metabolites tested, acetylcarnitine (urine), taurine (serum), hypoxanthine (serum), and carnitine (urine and serum) exhibited excellent to fair sensitivity and specificity. Urinary metabolites showed the greatest fold changes 1 d post-exposure while serum levels exhibited a more dynamic flux state along the 60 d time course. Indicators of purine deamination included perturbed levels of guanine (serum), hypoxanthine (serum), xanthine (urine), and 7-methylguanine (urine) after 4 Gy exposure. Xanthine can be formed from hypoxanthine oxidation or deaminated guanine and has been identified in other radiation metabolomic studies including NHP9, 12, human29, and mouse urine.30 Levels of xanthine fluctuate in urine with increases at 1 d and 28 – 60 d but decreased from 3 – 7 d, which was observed in a the previous NHP cohort at 7 d.12 Similarly, 7-methylguanine increased in urine at 1 d and 28 d and its presence has been implicated in DNA depurination, indicative of damage from endogenous processes.31 Another study found hypoxanthine formation through adenine deamination that may drive further downstream mutagenesis.32 Hypoxanthine has been found in lower levels in serum at 7 d13, however, here it was found at its lowest levels ~15 – 28 days along with guanine. These results indicate unique metabolomic signatures during the initial prodromal phase by possible increasing apoptosis or purine deamination, leading to increasing excretion rates in urine with levels returning to a perturbed state at a second phase ~15 – 28 days after exposure. Carnitine and acylcarnitines are integral to fatty acid β oxidation and have been implicated in numerous metabolic disorders33 as well as markers of radiation exposures (both external exposure and internal emitters) in biofluids from humans and multiple animal models.12– 14, 29, 34–38

As circulating carnitine levels are maintained through proper renal function, the 13



Page 14 of 33

increasing concentrations that have been observed may be partly due to radiation-induced renal toxicity39 in addition to fatty acid β oxidation disorders. Here we also found increases in two novel acylcarnitine metabolites in serum after irradiation, 3-dehydroxycarnitine and 2methylbutyroylcarnitine. 2-Methylbutyroylcarnitine has been putatively identified in autosomal dominant polycystic kidney disease40, while 3-dehydroxycarnitine reductions are lowered in skeletal muscle after exercise in db/db mice (Leprdb)41 and naturally higher in ob/ob mice (Leprob) (mutations leading to obesity and diabetes) compared to wild type.42 Although little biological information is known for these two unusual and branched-chain acylcarnitines (as well as other acylcarnitines), other than their association with perturbed energy metabolism and certain enzyme dysfunctions, the drastic increases across multiple animal models after IR exposure highlights their importance as robust radiation biomarkers of possible mitochondrial dysfunction. Both dimethyl-L-arginine (ADMA) and creatine increased in urine at 24 h postirradiation then returned to basal levels. ADMA was previously identified as lower in NHP serum post-exposure, however, concentration values of the previous study were below LOD thus not reported.14 ADMA inhibits nitric oxide synthase and may lead to endothelial or cardiovascular dysfunction.43 Previous reports show little change in urinary creatine levels at 0.5 d with a 3.5 Gy dose but increases at 1.5 d with a 6.5 Gy dose.9 Also, creatine was recently reported to increase in serum at 1 d after a 7.2 Gy exposure.44 Possible sources of creatine levels from direct damage to muscle and subsequent atrophy may confound its use when attempting a dose level prediction.45 Finally, serum levels of oleamide significantly increased 15 – 21 d postexposure. Oleamide is a fatty amide thought to interact with multiple neurotransmitter systems, suppress inflammatory cytokines46, and act synergistically with radiation exposure in limiting 14


Page 15 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


tumor growth.47 Possible roles for oleamide concentration fluctuations in response to radiation exposure remain to be explored. While previous studies have identified numerous compounds that create unique molecular signatures after radiation exposure8, validation cohorts are required to assess performance for future clinical utility. Validation cohorts for repeatability studies have been lacking for NHP studies due to ethical and financial factors. We established a quantitative method for a panel of metabolites (hypoxanthine, carnitine, acetylcarnitine, proline, taurine, and citrulline) due to their observed high-fold changes in the training NHP cohort and consistent identification in humans and other animal models. In the training cohort, carnitine and acetylcarnitine show increases in both urine and serum, with the greatest fold change in urinary acetylcarnitine levels (Figure S3). In the current validation cohort, urinary acetylcarnitine and serum carnitine have a combined excellent AUC value (0.99) indicating high sensitivity and specificity. Additionally, serum levels of taurine, hypoxanthine, and carnitine show high sensitivity and specificity in the validation cohort with an excellent AUC value (0.95). Serum acetylcarnitine levels were previously reported as slightly higher at 4 Gy14, however, there was not a significant change observed in the current study (P = 0.582, 1.1 fold change) and this compound may only be useful at higher exposure levels that would elicit hematopoietic and GI syndrome. One robust marker of radiation induced GI syndrome, citrulline48–50, showed high fold decreases and AUC values for both urine and serum in the previous cohort along with blood proline levels. The current cohort revealed similar trends in citrulline and proline levels but much lower fold changes. As circulating citrulline is released from enterocytes, their role at lower exposure levels with little GI damage may be small to negligible. These results highlight

15



Page 16 of 33

both the requirement of validation cohorts along the utility of multi-compound panels for classifying ARS severity from pre-exposure. 5. Conclusion For MS based metabolomics to transition to practical clinical use for biodosimetry, a panel of diagnostic biomarkers must be assembled and validated to create ready-to-use “kits” that can be processed with available software and instrumentation. The use and repurposing of existing infrastructure and products are critical for health emergency preparedness as current budgets are limited and new technology development can be time consuming.51, 52 Commercially available MS platforms currently in clinical use could serve as a valuable tool for differentiating ARS severity, as they would be operational with trained staff. In this study, we found that urinary metabolic signatures are more appropriate for differentiating exposed vs. nonexposed at earlier time points (1 d) while serum signatures are more dynamic and may be useful at later time points (e.g., 7 d) at 4 Gy. As single compounds are not sufficient to precisely determine radiation exposure in individuals without a priori knowledge, we developed a targeted LC-MS/MS MRM method in addition to our previous multiplex DMS MS assay28 to rapidly obtain quantitative values for a panel of metabolites and applied it to a validation NHP cohort. Combined metabolite ROC curve analysis showed excellent classification of minor ARS from pre-exposure samples to 7 d post 4 Gy exposure. Future efforts will incorporate additional compounds into our quantitative LC-MS/MS method to further refine dose estimates. Associated Content Supporting Information Table S1. Limit of detection, limit of quantification, range, and linearity Table S2. Levels of validated compounds in NHP biofluids after 4 Gy TBI 16


Page 17 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table S3. Significant compounds detected in at 7 d post 4 Gy exposure in the previous cohort Figure S1 - Rank of validated compounds by Random Forests analysis Figure S2 - Heatmap from Random Forests analysis of serum Figure S3 - Targeted quantitative analysis and ROC curves of metabolites previously identified in NHP urine and serum at 7 d post 4 Gy exposure Notes The authors declare that they have no conflict of interest These mass spectrometry data have been deposited to the NIH data repository via Metabolomics Workbench with the data set identifier epannkuk_20190227_104940 (untargeted urine), epannkuk_20190228_060422 (untargeted serum), epannkuk_20190227_122530 (targeted urine), epannkuk_20190228_054732 (targeted serum). Acknowledgements This work was funded by a pilot grant from the Opportunity Funds Management Core of the Centers for Medical Countermeasures against Radiation, National Institute of Allergy and Infectious Diseases (NIAID) (grant # U19AI067773; P.I. ELP), under HHS Contract (HHSN272201500013I) awarded to SRI International and NIAID grant # 1RO1AI101798 (P.I. AJF). The authors acknowledge Lombardi Comprehensive Cancer Metabolomics Shared Resource (MSR) for help with data acquisition, which has partial support from National Cancer Institute grant # P30CA051008 (P.I. Louis Weiner). Content is the responsibility of authors and does not necessarily represent official views of NIH.

17



Page 18 of 33

References (1) Friesecke, I.; Beyrer, K.; Fliedner, T. M.; METREPOL Team. How to cope with radiation accidents: the medical management. Br J Radiol. 2001, 74, 121-122. (2) DiCarlo, A. L.; Ramakrishnan, N.; Hatchett, R. J. Radiation combined injury: Overview of NIAID research. Health Phys. 2010, 98, 863-867. (3) Hafer, N.; Cassatt, D.; Dicarlo, A.; Ramakrishnan, N.; Kaminski, J.; Norman, M. K.; Maidment, B.; Hatchett, R. NIAID/NIH radiation/nuclear medical countermeasures product research and development program. Health Phys. 2010, 98, 903-905. (4) Goans, R. E.; Flynn, D. F. Medical Consequences of Radiological and Nuclear Weapons; Office of The Surgeon General: Fort Detrick, MD, 2012. (5) Donnelly, E. H.; Nemhauser, J. B.; Smith, J. M.; Kazzi, Z. N.; Farfan, E. B.; Chang, A. S.; Naeem, S. F. Acute radiation syndrome: Assessment and management. South Med J. 2010, 103, 541-546. (6) Macia, I. G. M.; Lucas Calduch, A.; Lopez, E. C. Radiobiology of the acute radiation syndrome. Rep Pract Oncol Radiother. 2011, 16, 123-130. (7) Port, M.; Pieper, B.; Dörr, H. D.; Hübsch, A.; Majewski, M.; Abend, M. Correlation of radiation dose estimates by DIC with the METREPOL hematological classes of disease severity. Radiat Res. 2018, 189, 449-455. (8) Pannkuk, E. L.; Fornace Jr, A. J.; Laiakis, E. C. Metabolomic applications in radiation biodosimetry: exploring radiation effects through small molecules. Int J Radiat Biol. 2017, 1-26. (9) Johnson, C. H.; Patterson, A. D.; Krausz, K. W.; Kalinich, J. F.; Tyburski, J. B.; Kang, D. W.; Luecke, H.; Gonzalez, F. J.; Blakely, W. F.; Idle, J. R. Radiation metabolomics. 5.

18


Page 19 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Identification of urinary biomarkers of ionizing radiation exposure in nonhuman primates by mass spectrometry-based metabolomics. Radiat Res. 2012, 178, 328-340. (10) Singh, V. K.; Newman, V. L.; Berg, A. N.; MacVittie, T. J. Animal models for acute radiation syndrome drug discovery. Expert Opin Drug Discov. 2015, 10, 497-517. (11) Singh, V. K.; Olabisi, A. O. Nonhuman primates as models for the discovery and development of radiation countermeasures. Expert Opin Drug Discov. 2017, 12, 695-709. (12) Pannkuk, E. L.; Laiakis, E. C.; Authier, S.; Wong, K.; Fornace Jr, A. J. Global metabolomic identification of longer-term dose dependent urinary biomarkers in non-human primates exposed to ionizing radiation Radiat Res. 2015, 184, 121-131. (13) Pannkuk, E. L.; Laiakis, E. C.; Mak, T. D.; Astarita, G.; Authier, S.; Wong, K.; Fornace, A. J. A lipidomic and metabolomic serum signature from nonhuman primates exposed to ionizing radiation Metabolomics. 2016, 12, 1-11. (14) Pannkuk, E. L.; Laiakis, E. C.; Authier, S.; Wong, K.; Fornace Jr, A. J. Targeted metabolomics of nonhuman primate serum after exposure to ionizing radiation: Potential tools for high-throughput biodosimetry RSC Advances. 2016, 6, 51192-51202. (15) Pannkuk, E. L.; Laiakis, E. C.; Authier, S.; Wong, K.; Fornace Jr, A. J. Gas chromatography/mass spectrometry metabolomics of urine and serum from nonhuman primates exposed to ionizing radiation: Impacts on the tricarboxylic acid cycle and protein metabolism. J Proteome Res. 2017, 16, 2091-2100. (16) Guijas, C.; Montenegro-Burke, J. R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A. E.; Wolan, D. W.; Spilker, M. E.; Benton, H. P.; Siuzdak, G. METLIN: A technology platform for identifying knowns and unknowns. Anal Chem. 2018, 90, 3156-3164. 19



Page 20 of 33

(17) Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; Sayeeda, Z.; Lo, E.; Assempour, N.; Berjanskii, M.; Singhal, S.; Arndt, D.; Liang, Y.; Badran, H.; Grant, J.; Serra-Cayuela, A.; Liu, Y.; Mandal, R.; Neveu, V.; Pon, A.; Knox, C.; Wilson, M.; Manach, C.; Scalbert, A. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018, 46, D608-D617. (18) Mak, T. D.; Laiakis, E. C.; Goudarzi, M.; Fornace, A. J. MetaboLyzer: A novel statistical workflow for analyzing postprocessed LC-MS metabolomics data. Anal Chem. 2014, 86, 506513. (19) Croft, D.; Mundo, A. F.; Haw, R.; Milacic, M.; Weiser, J.; Wu, G.; Caudy, M.; Garapati, P.; Gillespie, M.; Kamdar, M. R.; Jassal, B.; Jupe, S.; Matthews, L.; May, B.; Palatnik, S.; Rothfels, K.; Shamovsky, V.; Song, H.; Williams, M.; Birney, E.; Hermjakob, H.; Stein, L.; D’Eustachio, P. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014, 42, D472-7. (20) Caspi, R.; Altman, T.; Billington, R.; Dreher, K.; Foerster, H.; Fulcher, C. A.; Holland, T. A.; Keseler, I. M.; Kothari, A.; Kubo, A.; Krummenacker, M.; Latendresse, M.; Mueller, L. A.; Ong, Q.; Paley, S.; Subhraveti, P.; Weaver, D. S.; Weerasinghe, D.; Zhang, P.; Karp, P. D. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014, 42, D459-71. (21) Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27-30. (22) Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D. D.; Psychogios, N.; Dong, E.; Bouatra, S.; Mandal, R.; Sinelnikov, I.; Xia, J.; Jia, L.; Cruz, J. A.; Lim, E.; Sobsey, C. A.; Shrivastava, S.; Huang, P.; Liu, P.; Fang, L.; Peng, J.; Fradette, R.; Cheng, D.; Tzur, D.; Clements, M.; Lewis, A.; De Souza, A.; Zuniga, A.; Dawe, M.; Xiong, Y.; 20


Page 21 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Clive, D.; Greiner, R.; Nazyrova, A.; Shaykhutdinov, R.; Li, L.; Vogel, H. J.; Forsythe, I. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 2009, 37, D603-10. (23) Degtyarenko, K.; de Matos, P.; Ennis, M.; Hastings, J.; Zbinden, M.; McNaught, A.; Alcantara, R.; Darsow, M.; Guedj, M.; Ashburner, M. ChEBI: A database and ontology for chemical entities of biological interest. Nucleic Acids Res. 2008, 36, D344-50. (24) Breiman, L. Random forests Machine Learning. 2001, 45, 5-32. (25) Chong, J.; Soufan, O.; Li, C.; Caraus, I.; Li, S.; Bourque, G.; Wishart, D. S.; Xia, J. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018, 46, W486-W494. (26) Michaelson, S. M.; Odland, L. T. Relationship between metabolic rate and recovery from radiation injury. Radiat Res. 1962, 16, 281-285. (27) Williams, J. P.; Brown, S. L.; Georges, G. E.; Hauer-Jensen, M.; Hill, R. P.; Huser, A. K.; Kirsch, D. G.; Macvittie, T. J.; Mason, K. A.; Medhora, M. M.; Moulder, J. E.; Okunieff, P.; Otterson, M. F.; Robbins, M. E.; Smathers, J. B.; McBride, W. H. Animal models for medical countermeasures to radiation exposure. Radiat Res. 2010, 173, 557-578. (28) Chen, Z.; Coy, S. L.; Pannkuk, E. L.; Laiakis, E. C.; Fornace Jr, A. J.; Vouros, P. Differential mobility spectrometry-mass spectrometry (DMS-MS) in radiation biodosimetry: Rapid and high-throughput quantitation of multiple radiation biomarkers in nonhuman primate urine. Journal of the American Society for Mass Spectrometry. 2018, 29, 1650-1664. (29) Laiakis, E. C.; Mak, T. D.; Anizan, S.; Amundson, S. A.; Barker, C. A.; Wolden, S. L.; Brenner, D. J.; Fornace, A. J., Jr Development of a metabolomic radiation signature in urine from patients undergoing total body irradiation Radiat Res. 2014, 181, 350-361.

21



Page 22 of 33

(30) Tyburski, J. B.; Patterson, A. D.; Krausz, K. W.; Slavik, J.; Fornace, A. J., Jr; Gonzalez, F. J.; Idle, J. R. Radiation metabolomics. 2. Dose- and time-dependent urinary excretion of deaminated purines and pyrimidines after sublethal gamma-radiation exposure in mice. Radiat Res. 2009, 172, 42-57. (31) Lindahl, T. Instability and decay of the primary structure of DNA. Nature. 1993, 362, 709715. (32) DeVito, S.; Woodrick, J.; Song, L.; Roy, R. Mutagenic potential of hypoxanthine in live human cells. Mutat Res. 2017, 803-805, 9-16. (33) El-Hattab, A. W.; Scaglia, F. Disorders of carnitine biosynthesis and transport. Mol Genet Metab. 2015, 116, 107-112. (34) Pannkuk, E. L.; Laiakis, E. C.; Fornace Jr, A. J.; Fatanmi, O. O.; Singh, V. K. A metabolomic serum signature from nonhuman primates treated with a radiation countermeasure, gamma-tocotrienol, and exposed to ionizing radiation. Health Phys. 2018, 115, 3-11. (35) Laiakis, E. C.; Pannkuk, E. L.; Chauthe, S. K.; Wang, Y. W.; Lian, M.; Mak, T. D.; Barker, C. A.; Astarita, G.; Fornace Jr, A. J. A serum small molecule biosignature of radiation exposure from total body irradiated patients. J Proteome Res. 2017, 16, 3805-3815. (36) Mak, T. D.; Tyburski, J. B.; Krausz, K. W.; Kalinich, J. F.; Gonzalez, F. J.; Fornace Jr, A. J. Exposure to ionizing radiation reveals global dose- and time-dependent changes in the urinary metabolome of rat Metabolomics. 2014, 11, 1082-1094. (37) Goudarzi, M.; Mak, T. D.; Chen, C.; Smilenov, L. B.; Brenner, D. J.; Fornace Jr, A. J. The effect of low dose rate on metabolomic response to radiation in mice. Radiat Environ Biophys. 2014, 53, 645-657.

22


Page 23 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(38) Goudarzi, M.; Weber, W. M.; Mak, T. D.; Chung, J.; Doyle-Eisele, M.; Melo, D. R.; Brenner, D. J.; Guilmette, R. A.; Fornace Jr, A. J. Metabolomic and lipidomic analysis of serum from mice exposed to an internal emitter, cesium-137, using a shotgun LC-MSE approach. J Proteome Res. 2015, 14, 374-384. (39) Dawson, L. A.; Kavanagh, B. D.; Paulino, A. C.; Das, S. K.; Miften, M.; Li, X. A.; Pan, C.; Ten Haken, R. K.; Schultheiss, T. E. Radiation-associated kidney injury. Int J Radiat Oncol Biol Phys. 2010, 76, S108-15. (40) Gronwald, W.; Klein, M. S.; Zeltner, R.; Schulze, B. D.; Reinhold, S. W.; Deutschmann, M.; Immervoll, A. K.; Böger, C. A.; Banas, B.; Eckardt, K. U.; Oefner, P. J. Detection of autosomal dominant polycystic kidney disease by NMR spectroscopic fingerprinting of urine. Kidney Int. 2011, 79, 1244-1253. (41) Xiang, L.; Zhang, H.; Wei, J.; Tian, X. Y.; Luan, H.; Li, S.; Zhao, H.; Cao, G.; Chung, A. C. K.; Yang, C.; Huang, Y.; Cai, Z. Metabolomics studies on db/db diabetic mice in skeletal muscle reveal effective clearance of overloaded intermediates by exercise. Anal Chim Acta. 2018, 1037, 130-139. (42) Haley, M. J.; Mullard, G.; Hollywood, K. A.; Cooper, G. J.; Dunn, W. B.; Lawrence, C. B. Adipose tissue and metabolic and inflammatory responses to stroke are altered in obese mice. Dis Model Mech. 2017, 10, 1229-1243. (43) Vallance, P.; Leone, A.; Calver, A.; Collier, J.; Moncada, S. Endogenous dimethylarginine as an inhibitor of nitric oxide synthesis. J Cardiovasc Pharmacol. 1992, 20 Suppl 12, S60-2. (44) Pannkuk, E. L.; Laiakis, E. C.; Garcia, M.; Fornace Jr, A. J.; Singh, V. K. Nonhuman primates with acute radiation syndrome: Results from a global serum metabolomics study after 7.2 Gy total-body irradiation. Radiat Res. 2018, 190, 576-583. 23



Page 24 of 33

(45) Jurdana, M.; Cemazar, M.; Pegan, K.; Mars, T. Effect of ionizing radiation on human skeletal muscle precursor cells. Radiol Oncol. 2013, 47, 376-381. (46) Moon, S. M.; Lee, S. A.; Hong, J. H.; Kim, J. S.; Kim, D. K.; Kim, C. S. Oleamide suppresses inflammatory responses in LPS-induced RAW264.7 murine macrophages and alleviates paw edema in a carrageenan-induced inflammatory rat model. Int Immunopharmacol. 2018, 56, 179-185. (47) Lee, Y. J.; Chung, D. Y.; Lee, S. J.; Ja Jhon, G.; Lee, Y. S. Enhanced radiosensitization of p53 mutant cells by oleamide. Int J Radiat Oncol Biol Phys. 2006, 64, 1466-1474. (48) Jones, J. W.; Tudor, G.; Bennett, A.; Farese, A. M.; Moroni, M.; Booth, C.; MacVittie, T. J.; Kane, M. A. Development and validation of a LC-MS/MS assay for quantitation of plasma citrulline for application to animal models of the acute radiation syndrome across multiple species. Anal Bioanal Chem. 2014, 406, 4663-4675. (49) Jones, J. W.; Tudor, G.; Li, F.; Tong, Y.; Katz, B.; Farese, A. M.; MacVittie, T. J.; Booth, C.; Kane, M. A. Citrulline as a biomarker in the murine total-body irradiation model: Correlation of circulating and tissue citrulline to small intestine epithelial histopathology. Health Phys. 2015, 109, 452-465. (50) Jones, J. W.; Bennett, A.; Carter, C. L.; Tudor, G.; Hankey, K. G.; Farese, A. M.; Booth, C.; MacVittie, T. J.; Kane, M. A. Citrulline as a biomarker in the non-human primate total- and partial-body irradiation models: Correlation of circulating citrulline to acute and prolonged gastrointestinal injury. Health Phys. 2015, 109, 440-451. (51) DiCarlo, A. L.; Cassatt, D. R.; Dowling, W. E.; Esker, J. L.; Hewitt, J. A.; Selivanova, O.; Williams, M. S.; Price, P. W. Challenges and benefits of repurposing products for use during a

24


Page 25 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


radiation public health emergency: Lessons learned from biological threats and other disease treatments. Radiat Res. 2018, 190, 659-676. (52) Price, P. W.; DiCarlo, A. L. Challenges and benefits of repurposing licensed/approved/cleared products for a radiation indication. Radiat Res. 2018, 190, 654-658. (53) Vera, N. B.; Chen, Z.; Pannkuk, E. L.; Laiakis, E. C.; Fornace Jr, A. J.; Erion, D. M.; Coy, S. L.; Pfefferkorn, J. A.; Vouros, P. Differential mobility spectrometry (DMS) reveals the elevation of urinary acetylcarnitine in non-human primates (NHPs) exposed to radiation. J Mass Spectrom. 2018, 53, 548-559.

25



Page 26 of 33

TABLE 1 Multiple reaction monitoring transitions and parameters for compounds and internal standards in ESI+. Metabolite

Parent ion Daughter ion Dwell Time Cone Collision Retention (m/z) (m/z) (sec) (eV) (eV) time (min) Hypoxanthine 137.110 109.930 0.009 44 20 0.93 Hypoxanthine-d3 138.831 111.859 0.025 100 20 0.94 Carnitine 162.260 84.990 0.025 34 20 1.08 Carnitine-d3 164.905 85.038 0.025 94 14 1.09 Acetylcarnitine 203.956 85.077 0.010 72 10 0.90 Acetyl-d3-carnitine 206.916 85.021 0.010 80 8 0.91 Proline 116.000 70.100 0.009 20 10 1.32 Proline-d3 119.000 73.100 0.009 20 10 1.33 Taurine 126.000 44.000 0.009 24 14 1.31 13 Taurine- C2 128.126 46.192 0.009 14 12 1.32 Citrulline 176.000 69.900 0.009 16 20 1.98 Citrulline-d7 183.055 76.950 0.009 28 20 1.99

26


Page 27 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Biofluid Urine

Serum

TABLE 2 Validated Compounds in Nonhuman Primate Serum Perturbed after Total Body Irradiation Metabolite name RT Experimental Calculated Mass error Formula min m/z m/z ppm Carnitine Acetylcarnitine Propionylcarnitine Isobutyrylcarnitine/Butyrylcarnitine Creatine 7-Methylguanine Dimethyl-L-arginine Xanthine* Carnitine Propionylcarnitine Isobutyrylcarnitine/Butyrylcarnitine 3-Dehydroxycarnitine 2-Methylbutyroylcarnitine Hypoxanthine Guanine Oleamide

0.33 0.35 0.48 0.92 0.33 0.35 0.33 0.37 0.32 0.48 0.94 0.32 1.97 0.43 0.41 6.60

162.1131 204.1238 218.1392 232.1549 132.0771 166.0735 203.1491 151.0257 162.1130 218.1392 232.1546 146.1179 246.1705 137.0463 152.0571 282.2796

162.1130 204.1236 218.1392 232.1549 132.0773 166.0729 203.1501 151.0256 162.1130 218.1392 232.1549 146.1181 246.1705 137.0463 152.0572 282.2797

0.62 0.98 0.00 0.00 -1.51 3.61 -4.92 0.66 0.00 0.00 -1.29 -1.36 0.00 0.00 -0.66 -0.35

C7H15NO3 C9H17NO4 C10H19NO4 C11H21NO4 C4H9N3O2 C6H7N5O C8H18N4O2 C5H4N4O2 C7H15NO3 C10H19NO4 C11H21NO4 C7H15NO2 C12H23NO4 C5H4N4O C5H5N5O C18H35NO

HMDB ID 0000062 0000201 0000824 0000064 0000897 0001539 0000292 0000062 0000824 0006831 0000378 0000157 0000132 0002117

* H- adducts, remaining H+ adducts

27



Page 28 of 33

Figure 1. Random Forests analysis of urine with A) multidimensional scaling (MDS) plot and B) heatmap. Both represent the top 100 ranked ions in positive mode. The 1 d post-exposure samples separate from pre-exposure the most along dimension 2 in the MDS plot and the heatmap shows higher concentrations for many compounds at this time point. Post-exposure groups are more similar to pre-exposure at 3 – 7 d, separate along dimension 1 from 15 – 28 d, then return to pre-exposure at 60 d. Figure 2. Urine metabolites perturbed in an nonhuman primate model after 4 Gy total body irradiation at 1, 3, 5, 7, 15, 21, 28, and 60 d. The primary perturbations to metabolites occur at 1 d as observed in all metabolites. (* P < 0.05 determined by a Welch’s t-test between groups, Mean ± S.E., pre-exposure samples [-8 and -3 d] were averaged for the control group) Figure 3. Serum metabolites perturbed in a nonhuman primate model after 4 Gy total body irradiation at 1, 3, 5, 7, 15, 21, 28, and 60 d. More variation is observed across 60 d postexposure compared to urine as carnitine and acylcarnitines primarily fluctuate within 7 d, guanine and oleamide significantly change after 7 d post-exposure, and hypoxanthine decreases through 60 d with the lowest values observed at 15 – 28 d. (* P < 0.05 determined by a Welch’s t-test between groups, Mean ± S.E., identification of 3-dehydroxycarnitine based on in silico MS/MS spectrum, pre-exposure samples [-8 and -3 d] were averaged for the control group) Figure 4. Targeted quantitative analysis and receiver operator characteristic curves of metabolites in nonhuman primate urine and serum at 7 d post 4 Gy exposure. A) training cohort urine, B) validation cohort urine, C) training cohort serum, D) validation cohort serum. (bar graphs generated in GraphPad Prism 6.0 and compared with a t-test, Mean ± S.E., and ROC curves generated in MetaboAnalyst, blue shading 95% confidence interval) 28


5d

-0.2 15d 1d 21d 28d

0.0 0.2 0.4 - X 0 .5 0 5 6 8 2 5 7 5 - X 0 .2 3 9 4 6 9 5 3 8 - X 0 .1 1 1 9 7 3 5 1 9 - X 1 .9 8 2 6 3 5 5 0 8 - X 0 .2 1 6 0 4 4 0 9 5 1 - X 0 .1 5 6 4 0 0 4 6 - X 0 .8 8 3 0 3 0 7 8 5 - X 0 .3 1 2 4 0 5 1 4 8 - X 0 .2 2 9 3 5 1 9 6 1 1 - X 0 .1 6 7 8 4 0 1 - X 0 .0 6 4 4 2 8 1 0 6 - X 0 .9 4 4 3 1 1 3 8 7 - X 0 .3 8 1 5 3 8 1 6 9 - X 1 .9 0 5 4 3 7 8 8 6 - X 0 .1 4 8 4 3 4 9 5 7 - X 0 .2 7 9 5 8 3 4 4 9 - X 0 .1 3 9 2 0 6 9 6 7 - X 1 .4 8 0 1 9 1 5 3 8 - X 0 .8 1 2 4 4 8 2 8 9 - X 0 .2 6 4 5 1 5 9 3 5 - X 0 .0 4 0 5 0 2 2 4 2 - X 0 .1 2 9 9 4 7 5 7 7 - X 0 .1 5 9 5 0 4 0 7 3 - X 0 .0 5 5 3 8 8 5 6 1 - X 0 .0 4 9 0 3 7 3 2 4 - X 0 .8 1 1 0 9 6 3 6 4 - X 0 .2 4 1 9 7 5 1 2 1 - X 0 .0 0 1 9 9 7 9 9 5 - X 0 .1 2 7 8 6 3 4 6 8 - X 0 .4 1 5 4 7 6 3 5 9 - X 0 .0 4 3 9 0 6 7 2 9 - X 0 .0 4 3 4 6 3 4 9 7 - X 0 .0 7 4 0 3 7 3 7 9 - X 0 .3 9 3 4 5 9 9 6 2 - X 1 .0 2 6 3 3 9 3 8 4 - X 0 .0 6 1 5 1 9 9 4 9 - X 0 .4 4 2 2 9 6 5 0 2 - X 0 .6 6 6 0 4 2 2 0 6 - X 0 .2 8 0 5 9 7 8 3 3 - X 0 .1 5 0 9 0 7 7 7 9 - X 0 .3 2 8 7 7 4 3 7 3 - X 0 .3 6 6 7 0 1 0 5 4 - X 0 .9 1 0 5 1 9 9 6 3 - X 0 .0 9 8 6 8 3 5 8 7 - X 0 .3 8 2 6 5 6 4 5 3 - X 0 .5 9 6 0 3 2 6 0 8 - X 0 .1 5 2 7 3 5 5 1 4 - X 0 .2 5 6 3 3 5 6 1 8 - X 0 .5 6 1 3 7 6 5 1 1 - X 0 .1 1 3 4 8 1 5 7 5 - X 0 .1 6 0 3 9 0 7 1 2 - X 0 .1 0 8 5 1 4 8 5 1 - X 0 .2 3 1 4 5 2 5 2 5 - X 0 .3 9 5 8 8 7 0 9 9 - X 0 .7 0 0 3 3 4 6 1 6 - X 0 .4 4 7 7 0 8 8 2 7 - X 0 .2 3 2 3 1 0 6 4 7 - X 0 .2 9 6 0 8 9 1 5 2 - X 0 .3 8 9 6 9 5 7 7 7 - X 0 .0 9 4 0 5 3 6 9 1 - X 0 .2 3 1 3 8 6 7 3 8 - X 0 .0 5 7 3 2 0 0 2 4 - X 0 .1 3 2 1 3 7 1 5 1 - X 0 .2 8 5 0 1 9 2 3 2 - X 0 .8 5 8 3 4 9 7 2 5 - X 0 .1 0 0 9 9 5 3 5 2 - X 0 .2 2 4 6 8 9 0 1 2 - X 0 .2 7 7 7 0 5 0 5 2 - X 1 .0 1 1 6 7 6 2 6 9 - X 0 .1 4 6 3 2 7 3 1 3 - X 0 .1 4 1 5 1 6 4 4 5 - X 0 .6 1 1 6 0 4 4 1 6 - X 0 .2 5 4 9 6 4 8 4 1 - X 0 .7 3 7 0 5 4 1 5 8 9 - X 0 .2 5 1 2 5 9 7 9 - X 0 .3 0 2 7 8 6 9 8 9 - X 0 .1 8 3 0 2 3 8 1 7 9 - X 0 .2 0 9 6 9 8 7 4 9 - X 0 .4 0 0 2 9 8 5 3 9 9 - X 1 .7 4 6 3 8 1 7 0 2

0.2

0.4

3d

Color Key

0

3d 5d

Dim 1 Dimension 1

0 0.2 0.4

0.4

1d Value

Color Key

Value 0.6

1 - X0 1 - X0.1 1 - X0.2 1 - X0.3 1 - X0.4 1 - X0.5 1 - X0.6 1 - X0.7 1 - X0.8 24 - X0.9 24 - X0.10 24 - X0.11 24 - X0.12 24 - X0.13 24 - X0.14 24 - X0.15 24 - X0.16 24 - X0.17 36 - X35703.10549 36 - X55826.03711 36 - X23897.77148 36 - X29363.00586 36 - X24110.27539 36 - X79156.82031 36 - X57358.51691 36 - X77795.76172 36 - X63240.84375 48 - X77687.56605 48 - X43111.8125 48 - X48816.36125 48 - X50671.07405 48 - X88757.66711 48 - X74852.04668 48 - X74945.15679 48 - X43816.13086 48 - X49198.00601 96 - X0.18 96 - X0.19 96 - X0.20 96 - X0.21 96 - X0.22 96 - X0.23 96 - X0.24 96 - X0.25 96 - X0.26

B

1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 9 9

0.0

Top 100 Ions From RF - 72.2% Accuracy

1 1 1

Correct Misclassified

1 1 1 1 1

-0.2

A

1 - X0.505682575 1 - X0.239469538 1 - X0.111973519 1 - X1.982635508 1 - X0.216044095 1 - X0.15640046 1 - X0.883030785 1 - X0.312405148 1 - X0.229351961 1 - X0.1678401 1 - X0.064428106 1 - X0.944311387 1 - X0.381538169 1 - X1.905437886 1 - X0.148434957 1 - X0.279583449 2 - X0.139206967 2 - X1.480191538 2 - X0.812448289 2 - X0.264515935 2 - X0.040502242 2 - X0.129947577 2 - X0.159504073 2 - X0.055388561 3 - X0.049037324 3 - X0.811096364 3 - X0.241975121 3 - X0.001997995 3 - X0.127863468 3 - X0.415476359 3 - X0.043906729 3 - X0.043463497 4 - X0.074037379 4 - X0.393459962 4 - X1.026339384 4 - X0.061519949 4 - X0.442296502 4 - X0.666042206 4 - X0.280597833 4 - X0.150907779 5 - X0.328774373 5 - X0.366701054 5 - X0.910519963 5 - X0.098683587 5 - X0.382656453 5 - X0.596032608 5 - X0.152735514 5 - X0.256335618 6 - X0.561376511 6 - X0.113481575 6 - X0.160390712 6 - X0.108514851 6 - X0.231452525 6 - X0.395887099 6 - X0.700334616 6 - X0.447708827 7 - X0.232310647 7 - X0.296089152 7 - X0.389695777 7 - X0.094053691 7 - X0.231386738 7 - X0.057320024 7 - X0.132137151 7 - X0.285019232 8 - X0.858349725 8 - X0.100995352 8 - X0.224689012 8 - X0.277705052 8 - X1.011676269 8 - X0.146327313 8 - X0.141516445 8 - X0.611604416 9 - X0.254964841 9 - X0.737054158 9 - X0.2512597 9 - X0.30278698 9 - X0.183023817 9 - X0.20969874 9 - X0.400298539 9 - X1.746381702

-0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Dimension 2 Dim 2

Page 29 of 33 Journal of Proteome Research

Fig 1

125.02152_0.25_pos 388.28274_7.43_pos 366.30078_7.43_pos 410.32461_6.89_pos 388.34256_6.89_pos 390.3112_8.02_pos 565.49484_7.92_pos 368.31642_7.66_pos 619.50293_8.06_pos 394.329_7.38_pos 224.16267_4.97_pos 350.30541_7.64_pos 331.26411_7.51_pos 404.27769_7.16_pos 335.29512_7.55_pos 308.29543_7.79_pos 212.16494_5.39_pos 252.15739_5.39_pos 166.15956_5.39_pos 268.12462_5.39_pos 746.51271_9.06_pos 790.55932_8.79_pos 123.0445_0.29_pos 165.05505_0.29_pos 226.04586_0.29_pos 480.30927_7.46_pos 147.0452_0.29_pos 182.08186_0.31_pos 495.29349_7.08_pos 427.2699_6.76_pos 241.10548_3.2_pos 166.08681_0.46_pos 229.15556_0.31_pos 218.13974_0.31_pos 830.56879_9.19_pos 804.55481_9.12_pos 854.57111_9.11_pos 215.01711_0.31_pos 1098.94335_5.96_pos 770.57096_9.48_pos 552.40308_8.22_pos 1092.19321_6.03_pos 1092.56842_6.03_pos 858.60209_9.48_pos 856.58663_9.22_pos 493.27841_7.59_pos 838.69308_5.42_pos 316.24894_5.99_pos 1083.53257_5.89_pos 357.27952_6.86_pos 431.27769_6.32_pos 770.5005_9.37_pos 748.4877_9.37_pos 836.54051_9.34_pos 902.57822_9.32_pos 728.45088_9.39_pos 706.43827_9.4_pos 792.4433_9.36_pos 748.41288_9.37_pos 750.54672_9.37_pos 868.51631_9.34_pos 318.16516_4.46_pos 307.17366_4.46_pos 465.19568_3.17_pos 509.22177_3.56_pos 329.18774_4.7_pos 495.24232_3.56_pos 407.1898_2.72_pos 275.11129_0.76_pos 227.10467_3.17_pos 627.32145_4.47_pos 393.15844_2.74_pos 261.13262_0.78_pos 409.27218_8.81_pos 371.31621_8.81_pos 259.19123_8.81_pos 129.05513_8.81_pos 469.32959_9.4_pos

0.8

21 d 15 d 28 d

7d 60 d Pre

60d 7d

Pre


403.19439_5.47_pos 403.19439_5.47_pos

265.16461_4.06_pos 265.16461_4.06_pos

317.1569_3.76_pos 317.1569_3.76_pos

391.15751_2.3_pos 391.15751_2.3_pos

287.14689_4.06_pos

403.23048_5.65_pos 403.23048_5.65_pos

131.07092_5.63_pos 131.07092_5.63_pos

229.10518_2.68_pos 229.10518_2.68_pos

229.10521_2.4_pos

287.14689_4.06_pos

229.10521_2.4_pos

245.0998_1.34_pos 245.0998_1.34_pos

259.11534_2.3_pos 259.11534_2.3_pos

227.08931_2.16_pos 227.08931_2.16_pos

227.08956_2.28_pos 227.08956_2.28_pos

174.0914_4.64_pos 174.0914_4.64_pos

529.2233_4.74_pos 529.2233_4.74_pos

181.08178_5.22_pos

186.11304_3.02_pos

83.08622_3.02_pos

273.07537_4.49_pos

166.08412_0.32_pos

181.08178_5.22_pos

166.08412_0.32_pos

180.09992_0.33_pos 180.09992_0.33_pos

1 3 5

186.11304_3.02_pos

144.99261_0.3_pos 144.99261_0.3_pos

273.07537_4.49_pos

83.08622_3.02_pos

246.24311_5.47_pos 246.24311_5.47_pos

158.11821_0.35_pos 158.11821_0.35_pos

148.13379_0.32_pos 148.13379_0.32_pos

203.17992_5.87_pos 203.17992_5.87_pos

437.21421_5.8_pos 437.21421_5.8_pos

313.19886_3.69_pos

257.21122_5.15_pos

286.15497_2.4_pos

470.19262_3.9_pos

310.15558_3.78_pos

313.19886_3.69_pos

310.15558_3.78_pos

294.16045_4.49_pos 294.16045_4.49_pos

286.15497_2.4_pos

257.21122_5.15_pos

350.1481_4.91_pos 350.1481_4.91_pos

470.19262_3.9_pos

352.12685_2.42_pos 352.12685_2.42_pos

316.1427_3.9_pos 316.1427_3.9_pos

348.13227_5.3_pos 348.13227_5.3_pos

296.13952_2.04_pos 296.13952_2.04_pos

366.14265_3.11_pos 366.14265_3.11_pos

310.15511_2.68_pos

390.11275_3.02_pos 390.11275_3.02_pos

296.14032_2.95_pos 296.14032_2.95_pos

85.02911_0.35_pos 85.02911_0.35_pos

265.25256_8.85_pos

310.15511_2.68_pos

265.25256_8.85_pos

144.06613_0.5_pos 144.06613_0.5_pos

705.2697_5.19_pos 705.2697_5.19_pos

198.94059_0.26_pos 198.94059_0.26_pos

214.99979_0.26_pos 214.99979_0.26_pos

231.08453_0.37_pos

212.11859_3.08_pos

231.08453_0.37_pos

7 15 21 28 60

212.11859_3.08_pos

Color Key

0 0.8 0.2 0.4 0.6 0.8 1 1

Value

29


Fig 2

Normalized Abundance

6.0

4.0

4.0

* * *

2.0


3.0

*

*

8.0

6.0

0.0

Acetylcarnitine

2.0

*

0.0

Propionylcarnitine

8.0

*

6.0

2.0

Isobutyrylcarnitine / Butyrylcarnitine

4.0

1.0

*

0.0

2.0

*

0.0

7-Methylguanine

0.4


Carnitine

*

8.0

4.0

0.1

2.0

0.0

0.0

Xanthine

*

* Dimethyl-L-arginine

0.6

1.0

0.4

0.5 0.0

*

6.0

*

0.2

Creatine

8.0

*

0.3

1.5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 33

0.2

Pre

1

3

5

7

15

21

28

60

0.0

Pre

1

Treatment

3

5

7

15

21

28

60

Treatment

30


Page 31 of 33

Fig 3

Isobutyrylcarnitine / Butyrylcarnitine

Carnitine


4.00

0.20

* * *

3.00 2.00 1.00

0.10

*

2-Methylbutyroylcarnitine


*

0.06 0.04

Propionylcarnitine

1.50

*

0.08

*

1.00

*

*

0.50

0.02 0.00

0.00

Guanine

0.15

*

0.05 0.00

3-Dehydroxycarnitine

* *

0.40

0.10

0.30

*

*

0.20

* *

0.10 0.00

Oleamide

1.50

Hypoxanthine

0.60

* *

1.00

*

0.40

0.50 0.00

*

*

*

0.05 0.00

0.10


*

0.15

0.00


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


*

* * * *

0.20

Pre

1

3

5

7

15

21

28

60

0.00

Pre

1

Treatment

3

5

7

15

21

28

* 60

Treatment

31



Fig 4

50 0

Acetylcarnitine

300 200 100 0

Control

1-Specificity

7d, 4 Gy

Treatment

400

200

0

2000

1000 500 0

60 40 20

Concentration (ng/ml)

AUC = 0.99

0

Hypoxanthine

1-Specificity

15 10 5

Control

7d, 4 Gy

Treatment


Carnitine

80

0

1-Specificity

7d, 4 Gy

Treatment

100

Taurine

80 60 40 20 0

100


0

Sensitivity

Concentration (ng/ml) Concentration (ng/ml)

50

20

Control

D Taurine

100

100

AUC = 0.99

1500

Serum C 150

Acetylcarnitine

2500

Carnitine

80 60 40 20

Sensitivity

400

AUC = 0.92

Carnitine

600

Sensitivity

100


150


B Carnitine

200

Sensitivity



Urine A


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 33

AUC = 0.95

0

20

Hypoxanthine

1-Specificity

15 10 5 0

Control

7d, 4 Gy

Treatment

32


Page 33 of 33

For TOC Only LC-QTOF-MS Global metabolomics

Top 100 Ions From RF - 72.2% Accuracy

Random Forests

3d 0.4

1d

Analysis

-0.2

Radiation

5d

Biofluids Tandem quad LC-MS Targeted metabolomics

28d

21d 15d

0.0

Dim 2

0.2

Sham

7d 60d Pre

-0.4

Correct Misclassified

15d 1d

-0.2

21d 28d

0.0

0.4

ROC Curve

100

50

0

Carnitine 60

15

40

20

0

Hypoxanthine

Sensitivity


Taurine

60d 7d

150


Analysis

3d 5d 0.2

Dim 1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


AUC = 0.99

10

5

0

Control

7d, 4 Gy

Treatment

1-Specificity

33


Liquid ChromatographyâMass Spectrometry-Based Metabolomics of

Recommend Documents

Liquid ChromatographyâMass Spectrometry-Based Metabolomics of