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Temporal Effects on Radiation Responses in Nonhuman Primates: Identification of Biofluid Small Molecule Signatures by Gas Chromatography – Mass Spectrometry Metabolomics

Whole body exposure to ionizing radiation (IR) (> 0.7 Gy) damages tissues leading to a range of physical symptoms contributing to acute radiation syndrome (ARS). Radiation biodosimetry aims to determine characteristic early biomarkers indicative of radiation exposure (generally at doses > 2 Gy) and is a necessity for effective triage in the event of an unanticipated radiological incident and emergency preparedness. Radiation metabolomics can address this aim by assessing metabolic perturbations following various emergency scenarios (e.g., elapsed time to medical care, absorbed dose, combined injury). Gas chromatography – mass spectrometry (GC-MS) is a standardized platform ideal for chromatographic separation, identification, and quantification of metabolites to discriminate molecular signatures that can be utilized in assessing radiation injury. We performed GC time-of-flight (TOF) MS for global profiling of nonhuman primate (NHP) urine and serum samples up to 60 d after a single 4 Gy γ-ray total body exposure. Multivariate statistical analysis showed a higher separation of groups from urine signatures vs. serum signatures. We identified biofluid markers involved in amino acid, lipid, purine, and serotonin metabolism, some of which may indicate host microbiome dysbiosis. Sex differences were observed amino acid fold changes in serum samples. Additionally, we explored mitochondrial dysfunction by analysis of tricarboxylic acid (TCA) intermediates with a GC tandem quadrupole (TQ) MS platform in samples collected in a time course during the first week (1, 3, 5, and 7 d) after exposure. By adding this temporal component to our previous work exploring dose effects at a single time point of 7 d, we observed the highest fold changes occurring at 3 d, returning closer to basal levels by 7 d. These results emphasize the utility of both MS-based metabolomics for biodosimetry and complementary analytical platforms for increased metabolome coverage.


Introduction
Exposures to external ionizing radiation (IR) can include occupational hazards [1] or accidents from nuclear energy (e.g., deposition of 137 Cs after the Chernobyl accident) [2] to normal background radiation [3]. The lingering health risks associated with IR exposures among large populations can be observed in atomic bomb survivors many decades after the initial exposure, both in Hiroshima and Nagasaki [4,5]. More recently, the potential of malicious use of radioactive materials, such as a radiological dispersal device (RDD) or improvised nuclear device (IND), for terrorist actions has been of concern. As such, there is a need for predictive biomarkers indicative of radiation exposure to aid in the triage of large populations during a potential radiological terrorism act. The National Institute of Allergy and Infectious Diseases (NIAID) is leading this effort through the Centers for Medical Countermeasures Against Radiation Consortium (CMCRC) program [6]. Of these needs, our group has particular interest in the use of mass spectrometry (MS) platforms for radiation biomarker development, aiding in rapid high-throughput biodosimetry and assessment of acute radiation syndrome (ARS) and associated tissue damage and recovery [7].
Metabolomics is the collective analysis of small molecules (< 1 kDa) in biofluids, cells, or tissues that provides phenotypic signatures downstream of other -omic technologies, such as transcriptomics or proteomics [8]. By far, the two dominating platforms for metabolomics have been MS and nuclear magnetic resonance (NMR), and as each possesses unique analytical advantages and disadvantages, these platforms are viewed as complementary for increased metabolite coverage [9]. The advantages of NMR platforms include non-destructive sample analysis, high reproducibility, and ability to perform accurate quantification, however, low sensitivity leads to identification of similar profiles of high abundance metabolites and NMR analysis requires large volumes of material [10].  [11]. Of the above mentioned, GC-MS platforms have served as the earlier tools in metabolite profiling [12,13], and have become one of the most standardized and reproducible tools for metabolomic profiling [14].
In addition to its superior separation of isomers and small volatile compounds, the breadth of compounds available in reference databases far surpasses other metabolomic platforms [15]. As previous studies on radiation biofluid metabolomics using GC-MS platforms have been limited, including mice (plasma after low dose radiation exposure) [16], rats (urine time effects [17] and time/dose effects [18]; serum dose effects [19] responses to radiation injury as well as the importance of using multiple analytical platforms to obtain a more complete view of the metabolome.

NHP system
Rhesus monkeys (Macaca mulatta) were irradiated at an AAALAC accredited facility as Technology (NIST) spectral library v. 14 (Gaithersburg, MA, USA) and retention indices to FiehnLib [14]. Pre-exposure (-8 and -3 d) samples were averaged for the control reference.
Urine and serum were normalized to an internal standard and urine was further normalized to creatinine. For multivariate analysis, data matrices were imported into MetaboAnalyst 4.0 with K-nearest neighbor missing value imputation, log transformation, and Pareto scaled for visualization by partial least squares-discriminant analysis (PLS-DA) [27]. The performance of the PLS-DA model was assessed using 10-fold cross-validation (CV) and B/W ratio after 1000 permutations [28]. For univariate analyses, data matrices were initially screened for statistically significant compounds with an ANOVA using the proc glm function and a post-hoc Duncan test in SAS 9.4 (Cary, NC, USA). Significant compounds were graphed with GraphPad Prism 6.0 (GraphPad Software, Inc., La Jolla, CA, USA) with outliers removed using robust regression and outlier removal and analyzed with a Kruskal-Wallis test and pot-hoc Dunn's test across days and a t-test for sex differences. For analysis of TCA cycle intermediates, raw data files were processed and analyzed using TargetLynx v4.1 (Waters, Milford, MA, USA) and log fold changes were graphed with GraphPad Prism 6.0.

Results and Discussion
Data matrices consisted of 408 compounds for urine and 112 compounds for serum.
Creatinine was not significantly different among groups and was used for normalization of urine data. Multivariate PLS-DA analysis was performed to assess separation among pre-IR vs.  Figure 1C), while little separation was observed from 15 -60 d (10-fold CV: accuracy = 0.42, R 2 = 0.92, Q 2 = 0.08; performance measure: Q 2 P = 0.009; Figure 1D). While urine metabolite biosignatures are typically more "striking" than serum, as observed in this study, serum is a valuable biofluid when monitoring changes in lipid content after IR exposures and can discriminate exposed individuals from the unexposed at the metabolite level.
Univariate analyses showed significantly decreased levels of allantoic acid (P < 0.001) from 5 -60 d and higher levels of 5-hydroxyindoleacetic acid (P = 0.003) at 60 d in urine (Table   2, Figure 2A, Table S1). Allantoic acid is a metabolite produced through purine metabolism downstream of uric acid. Uric acid is enzymatically degraded or spontaneously converted to allantoin, which may be further metabolized to allantoic acid through allantoinase [29]. As Old World primates lack allantoinase (e.g., Rhesus macaques), it is possible that the presence of allantoic acid is due to microbial activity [30,31]. The compound 5-hydroxyindoleacetic acid is a metabolite of serotonin (5-hydroxytryptamine) formed by aldehyde dehydrogenase via 5hydroxyindoleacetaldehyde and as a possible downstream product of tryptophan metabolism may also be influenced by host microbiota [32,33]. Serotonin has previously received interest as a radioprotector [34,35] and elevated levels of 5-hydroxyindoleacetic acid has been shown in urine after radiation exposure [36][37][38]. Interestingly, kynurenic acid and xanthurenic acid are also metabolic products derived from tryptophan, possible indicators of renal damage, and are commonly detected in urine, as identified in previous radiation metabolomics studies [7]. Other metabolites in the tryptophan pathway due to host microbiota dysbiosis have been identified in plasma and future studies may continue to expand on these [39].
Amino acids, such as taurine and citrulline, show consistent perturbation due to radiation exposure and are conserved across species [7,40,41]. Our previous work has shown a general trend in decreased NHP serum amino acid levels (primarily in non-essential amino acids) at 7 d at several doses (2, 4, 6, 7, and 10 Gy) that would represent differing ARS severity [20,26,42].
However, fluctuating amino acids levels in the blood over time post-exposure [19,43] clearly portray a much more dynamic pathophysiological response. As a majority of the amino acids identified in this study (except serine) are essential amino acids, diet and nutrient absorption may play a role in observed serum amino acid concentration. Also, sex differences can contribute to basal amino acid levels, such as citrulline [44], and have differential responses after radiation  (Table   3). Comparing samples at 7 d to the previous cohort, fold changes were similar with decreases in citric and isocitric acid and negligible changes in fumaric and cis-aconitic acid, but decreases in malic and succinic acid were slightly greater than previously reported (Table S2). However, when comparing the temporal effects of TBI on urinary TCA cycle intermediates during the first week, the greatest fold changes were seen at 3 d with less pronounced decreases at 1 d (except isocitric acid) and returning closer to baseline levels at 5 -7 d ( and amino acid perturbation, allowing differentiation among pre-exposure and post-exposure groups and possible longer-term effects from radiation exposure.

Conclusions
The majority of studies defining biophysiological responses to radiation exposures have focused on LC-MS platforms [7], however, these methods lack in chromatographic separation of small volatile compounds or isomers (e.g., leucine vs. isoleucine) and availability of well- metabolite coverage, such as the TCA cycle intermediates described in this study. Subsequent compound analyses can be further refined for high-throughput targeted platforms more suited to biodosimetry, such as differential mobility spectrometry (DMS) MS [63,64] or LC-MS [24]. In this study, we examined global small molecule signatures in NHP biofluids (urine and serum) from 1 -60 d after a 4 Gy γ -ray TBI exposure and determined fold changes in urinary TCA cycle intermediates from 1 -7 d using GC-MS platforms. We found perturbed metabolites in NHP biofluids involved in amino acid, lipid, serotonin, and purine metabolism, some of which may reflect dietary and host microbiome changes. Primarily essential amino acids were identified in this study and females exhibited higher fold changes (including non-essential serine), but influences of diet and nutrient absorption on essential amino acid levels should be examined if used as a proxy for radiation injury without additional multivariate analyses [43,65].
Finally, we found the greatest perturbation of TCA intermediates in the first week at ~3 d and this trend may be consistent across species [57]. These results highlight the importance of using multiple analytical platforms when examining biophysiological responses to radiation injury and future work may integrate these results into a single platform for rapid biodosimetry.   Table 1 Multiple reaction monitoring transitions and parameters for pyruvic acid, TCA cycle intermediates, and deuterated citric acid (internal standard).  Table 2 Compounds detected by GC-TOF-MS global profiling of NHP biofluids after 4 Gy Table 3 Log fold changes (logFC) of urinary pyruvic acid and TCA cycle intermediates at 1, 3, 5, and 7 d after a 4 Gy γ radiation exposure in NHPs.