TY - JOUR
AU - Glaros,, Trevor
AB - Abstract Despite the recent epidemic of fentanyl abuse, there are few validated assays capable of rapidly detecting these compounds. In order to improve the ability to detect carfentanil at physiologically relevant concentrations, we developed a systems biology approach to discover host-based markers which are specifically amplified upon exposure in a rabbit model. For this work, two “omics” pipelines utilizing mass spectrometry were developed and leveraged. First, a proteomics pipeline was developed to interrogate the blood plasma for protein-based biomarkers. Due to the incredible dynamic range of the plasma protein content, a multi-dimensional fractionation technique was used to partition and more accurately investigate the circulating plasma proteome. Isobaric tandem mass tags were integrated into the workflow to make quantitative assessments across all animals for an extended time course post-exposure. In addition to the proteomics efforts, blood plasma was also processed through an untargeted metabolomics pipeline. This approach allows for the identification of >800 small molecule features. By processing and analyzing data sets in parallel, we were able to identify a unique fingerprint of protein and metabolite perturbations that manifest following exposure to carfentanil. carfentanil, biomarker, opioid, proteomics, metabolomics Currently the world is facing an unprecedented opioid epidemic. In a recent report by the U.S. Center for Disease Control, more than 500, 000 people have died due to drug overdoses between 1999 and 2015 (Seth et al., 2018). In 2015, there were a total of 52, 404 overdose deaths of which 63% involved opioids. The largest increase, 74%, in overdose deaths from 2014 to 2015 involved synthetic opioids, such as fentanyl. Synthetic opioids cover a broad class of pharmaceuticals which collectively target opioid receptors primarily for analgesic relief and anesthesia. New synthetic opioids were developed for use in the clinic as early as the 1960s. This new class of chemistry was designed to be more potent with less side effects and to target specific opioid receptors to have the desired clinical outcomes. Research from these programs resulted in the discovery and subsequent wide spread use of more potent fentanyl and related analogs. Building upon the successful development of fentanyl, carfentanil was synthesized in 1974 and found to be 100-fold more potent (Vanbever et al., 1976). As a result of its extreme potency, carfentanil was only approved for use in large animal veterinary medicine. Compared with naturally occurring opioids such as codeine and morphine, synthetic fentanyl analogs are relatively easy to manufacture using precursors and protocols that are widely accessible. As such, these chemistries became attractive candidates for illicit drug manufacturers. Based upon relative potency of fentanyl, it is predicted that just a kilogram of carfentanil can be cut into more than 30 million lethal doses (Casale et al., 2017). Since its introduction into the illicit drug trade, there have been several reports of large seizures by law enforcement including more than 42 kg of carfentanil in Pickering, Ontario, Canada (Doucette, 2017). Until the early 2000s, literature references of human exposures to carfentanil were limited entirely to the research of addiction. However, in October 2002, a group of Chechen militants wearing explosive vests and carrying automatic weapons took over 800 people hostage in the Dubrovka Theatre in Moscow, Russia (Dolnik and Pilch, 2003; Krechetnikov, 2012; Kutukova, 2003; Tavernise and Kishkovsky, 2002; Wax et al., 2003). The situation was eventually resolved when Russian anti-terrorism forces released an undisclosed aerosol through the theater’s ventilation system. Although few shots were fired in the ensuing rescue, 129 hostages died due to exposure to this mysterious aerosol. Although the Russian government offered little detail on the makeup of the released aerosol, in 2012, the British Defense Science and Technology Laboratory (DSTL), the United Kingdom’s chemical weapons defense research organization published on the nature of the aerosol. By analyzing clothing from 2 individuals at the scene, the authors concluded that the culprit agents used to help quell the crisis were remifentanil and carfentanil (Riches et al., 2012). Traditional analytical chemical methods such as gas- or liquid-chromatography tandem mass spectrometry (GC- or LC-MS/MS) are currently utilized to identify carfentanil and other opioids both in drug seizures and clinical samples of blood or urine (Thevis et al., 2005; Van Nimmen et al., 2004; Wang and Bernert, 2006). Generally speaking, mass spectrometric techniques coupled with chromatography result in detection limits which have been shown to be adequate for detecting clinically relevant levels of carfentanil and its major metabolite, norcarfentanil. A recent report that analyzed the blood from 355 patients, where carfentanil was suspected as the acute toxicant resulting in morbidity, revealed that 88% of the specimens had blood concentrations below 1 ng/ml, with a median concentration <0.1 ng/ml (Papsun et al., 2017). To date, police, first responders, and military largely rely on affinity or colorimetric-based assays for presumptive identification in the field (Philp and Fu, 2018). Currently, there are several tests which have been commercialized to detect fentanyl (BTNX, Inc. and Express Diagnostics International, Inc.); however, there are not any rapid screening assays which are commercialized that can detect carfentanil. Based upon the current state-of-the art of lateral flow assays (LFA), it may not be possible to develop a carfentanil LFA that can detect exposure to this ultra-potent opioid at sub-lethal levels. To bridge this gap, we have employed a systems biology approach to fully characterize the host-amplified biochemical response of rabbits exposed to low levels of carfentanil to identify a signature of exposure (Figure 1) (Zhao et al., 2018). To mimic the Moscow Theater route of exposure, rabbits were exposed to ultra-low levels of aerosolized carfentanil and serially bled for 13 days. Blood plasma was then used for shotgun quantitative proteomics and untargeted metabolomics. This methodology resulted in a panel of analysis of variance (ANOVA) statistically significant proteins and metabolites which correlated to carfentanil exposure. The results suggest a number of physiological implications associated with exposure that could be leveraged to develop more suitable detection and diagnostic tools. Figure 1. View largeDownload slide Schematic of rabbit lung exposure to low doses of carfentanil and the proteomics and metabolomics pipelines utilized to identify biomarkers. Figure 1. View largeDownload slide Schematic of rabbit lung exposure to low doses of carfentanil and the proteomics and metabolomics pipelines utilized to identify biomarkers. MATERIALS AND METHODS Animal Exposures Young adult male New Zealand White Rabbits, 2.5–2.7 kg, were procured from Covance, Inc. (Princeton, New Jersey) and pair-housed for 3 days prior to testing. Husbandry, feed/water provisions and sanitation schedules were carried out in accordance with the 2011 Eighth Edition of the Guide for the Care and Use of Laboratory Animals (National Research Council, 2010). Rabbits were on a cycle of 12 h of light and 12 h of dark. Individual rabbit rooms are maintained at 70°F ± 2°F with 30%–70% relative humidity. Rabbits were housed in a facility that is fully accredited by the Association for Assessment and Accreditation of Laboratory Care International (AAALAC). Rabbits were pair-housed in plastic cages on racks and provided with certified laboratory chow and reverse osmosis water ad libitum, except during testing. Animal care and use for these experiments was approved by the Institutional Animal Care and Use Committee (IACUC) for U.S. Army/ECBC. Animal exposures were conducted in accordance with a protocol approved by the ECBC IACUC. Exposures took place while mated to a nose-only exposure chamber, 20 cc inner volume, with a flow rate of 19 LPM and under negative pressure of −0.5” H2O. Chamber concentration was measured real-time using a TSI Dust-Trak II, model 8530 (Shoreview, Minneapolis). Aerosol generation was performed by injecting a 10 mg/ml gravimetrically prepared solution of carfentanil dissolved in methanol from a Hamilton 1000 series gastight syringe using a Harvard Apparatus syringe drive at a rate of 0.9 µl/min through the inner needle of a coaxial needle atomizing system. The outer needle compressed air (60 psi) exposes the liquid droplet to shear forces that atomize the liquid stream into a fine mist. For chamber concentration and particle sizing, glass-fiber filter pads in duplicate and a 7-stage cascade impactor were used respectively. Filter pads and stages were analyzed via LS-MS/MS using a 1200 series Agilent HPLC coupled to an Agilent Triple Quadrupole 6490. Reverse phase separation was conducted with a Waters (Beverly, Massachusetts) Acquity UPLC BEH 1.7 µm, 2.1 × 50 mm column, with a phase-matched VanGuard guard column, isothermal at 40°C. Mobile phase consisted of (a) dH2O with 0.1% FA and (b) ACN w/0.1% FA. The gradient was a 2-min elution starting with 10% B for 0.5 min, to 99% B at 1 min, hold for 0.5 min, and return to initial conditions/equilibrate for 0.5 min. All animal were exposed to a concentration of 0.18 mg/m3 of carfentanil for 5 min. For all animals this dose produced no physiological signs or symptoms. Blood Collection Blood was taken from each animal (N = 6) pre- and post-exposure using the marginal ear vein prick with a sterile 23–21 gauge needle and collected in 500 µl K2 EDTA tubes. For the pre-exposure control, 250 µl of blood was collected 24 h prior to exposure. Following exposure, blood was sampled immediately post-exposure (used for metabolomics only), +6 h, 1 day (D), 2D, 3D, 6D, 7D, 8D, 9D, and 13D. Euthanasia was performed, on day 14, in accordance with the American Veterinary Medical Association Guidelines for the Euthanasia of Animals: 2013 Edition (Leary et al., 2013). Rabbits did not have visual, auditory, or olfactory access to euthanasia of other rabbits. Method of euthanasia was cervical dislocation of the C1 vertebra using a stainless steel RP-3000 Rabbit and Poultry Wringer (MHS, LLC, West Grove, Pennsylvania). Death was verified by 3 methods: loss of pupillary light response, retrobulbar reflex, and loss of respiration/cardiac arrest. The BD P100 Blood Collection System was used to remove the blood from the syringe. The tube was inverted 3 times to ensure distribution of the anticoagulant (K2EDTA) and placed on ice. The tubes were then spun at 2500 × g for 20 min at 4°C to separate the plasma. The plasma was carefully removed from the tubes and aliquoted prior to storage at −80°C. Proteomics Sample preparation Plasma samples were prepared as described previously (Tran et al., 2018). Briefly, frozen plasma samples were thawed on ice and quantitated for total protein concentration using the BCA assay (Pierce, Rockford, Illinois). A 175 µg of each sample was removed for digestion and brought up to a total volume of 200 µl with 50 mM triethylammonium bicarbonate. The samples were then denatured by adding 300 µl 10 M urea and 10 µl 1 M dithiothreitol; they were heated at 56°C for 30 min while shaking. Samples were allowed to cool and were alkylated by adding 40 µl of 0.5 M iodoacetamide and incubated at room temperature in the dark for 30 min. Following alkylation, 1 ml of 50 mM triethylammonium bicarbonate containing 4 µg of trypsin/lysC (Promega, Madison, Wisconsin) was added to the samples and digestion occurred overnight at 37°C with shaking. Digestion was terminated by adding 15 µl of 100% formic acid using a glass syringe. Oasis HLB 1 cc (30 mg) reverse phase cartridges (Waters, Milford, Massachusetts) were used to desalt each sample following the manufacturer’s protocol. Eluted samples were dried in a speed-vac overnight. TMT labeling Each sample was reconstituted in 0.1 M triethylammonium bicarbonate + 10% acetonitrile to a final concentration of 30 µg/50 µl. A 50 µl aliquot was removed from each sample and was labeled using Thermo Fisher’s 10plex TMT kit following the manufacturer’s recommendations with some slight modifications. Briefly, each tag (0.8 mg) was re-suspended in 100 µl of anhydrous acetonitrile, vortexed for 5 min, and spun down at 14 000 × g for 30 s. Each 50 µl sample aliquot (30 µg of peptide) was mixed with 20 µl of a unique TMT tag and vortexed for 1 min, spun down briefly, and incubated at room temperature for 1 h with shaking. The reaction was then quenched with 4 µl of 5% hydroxylamine and allowed to incubate for 15 min at room temperature with shaking. All samples were combined (10 in total), dried in a speed-vac, and stored at −80°C. Basic reverse phase liquid chromatography (bRPLC) TMT-labeled peptide mixtures were fractionated by bRPLC as described in Keshishian et al. (2015). Briefly, the sample was reconstituted in 100 µl 100% acetonitrile, vortexed, and incubated at 37°C for 5 min. A 100 µl of 20 mm ammonium formate/10% acetonitrile pH 10 (buffer A) was then added to the sample which was again vortexed and incubated at 37°C for 5 min. Finally, another 800 µl of buffer A was added to the sample which was vortexed before being spun down in a centrifuge at 14 000 × g for 3 min. The sample was loaded by syringe pump onto a Waters XBridge C18 5 µm 4.6 × 250 mm column with a Waters XBridge C18 5 µm 4.6 −× 20 mm guard cartridge. Then the column was connected to an Agilent 1260 HPLC pump system (Santa Clara, California) equipped with an analytical-scale fraction collector. The flow rate was set to 200 µl/min. Buffer A was made up of 20 mM ammonium formate/10% acetonitrile pH 10, and Buffer B was made up of 20 mM ammonium formate/90% acetonitrile pH 10. Peptides were separated using the following gradient: 0–5 min: 0% B, 5–13 min: 0%–15% B, 13–46 min: 15%–28.5% B, 46–51.5 min: 28.5%–34% B, and 51.5–64.5 min: 34%–60% B. Samples eluted in the first 5.5 min were pooled into 3 “early” fractions and the final 9.2 min were pooled into 5 “late” fractions. Between the “early” and “late” fractions, single fractions were collected every 0.6 min for a total of 84 fractions. The 84 fractions were pooled following the early, mid, and late concatenation strategy as described previously for a total of 28 fractions (Wang et al., 2011). All fractions were acidified with 100 µl of 10% formic acid and dried in a speed-vac. Liquid chromatography mass spectrometry analysis (LC-MS) Immediately prior to LC-MS analysis each fraction was reconstituted in 20 µl of 95% water/5% acetonitrile with 0.1% formic acid and vortexed for 2 min to completely dissolve the sample. Samples were then centrifuged at 14 000 × g for 5 min and 18 µl was transferred to individual autosampler vials. The 3 “early” fractions were combined into a single vial, and the 5 “late” fractions were combined into another vial. Each fraction was analyzed on a Thermo Fisher Orbitrap Fusion mass spectrometer coupled to a Dionex Ultimate 3000 UHPLC (Thermo Fisher Scientific). Injections of each sample (2 µl) were first pre-concentrated on a reverse-phase trapping column (PepMap 300 µm × 5 mm C18, 100 Å, Thermo Fisher) and then resolved on a 75 µm × 500 mm EASY-Spray column packed with PepMap RSLC, C18, 2 µm, 100 Å particles (Thermo Fisher) using a 190 min multistep gradient [0–150 min: 6%–35%B, 150–158 min: 35%–60%B, 158–161 min: 60%–90%B, 161–171 min: 90% B hold, 171–172 min: 90%–96% B, and 172–182 min: 6% B hold] as described previously (Keshishian et al., 2015). For the gradient, the A buffer is 100% H2O/0.1% formic acid and the B buffer is 98% acetonitrile/2% H2O/0.1% formic acid. MS1 scans were acquired using the Orbitrap, which performed at a resolution of 120, 000 with a scan range of m/z 350–1500 in profile mode. The top 10 precursors were selected for MS2 data-dependent fragmentation. MS2 spectra was acquired using the ion trap with the scan rate set to turbo, with the first mass fixed to m/z 120 to capture TMT reporter ions. The minimum signal required to trigger a data-dependent scan was 5000. Monoisotopic precursor selector was set to “on” and charge state filter was set to include charge states 2–7. Collision-induced dissociation (CID) was used to generate MS2 spectra with collision energy set to 35%. MS3 spectra were also acquired in profile mode with Orbitrap resolution set to 60, 000 and scan range of m/z 120–500. Higher-energy C-trap dissociation (HCD) was used to generate MS3 spectra with collision energy set to 65%. AGC target was set to 200, 000 for MS1 and 10, 000 for both MS2 and MS3. The maximum accumulation time of MS1 and MS2 were 50 ms and 120 ms for MS3, respectively. Dynamic exclusion was set for 70 s with a 10 ppm mass window. Data processing Intensities of MS3-generated reporter ions were used for quantification using Proteome Discoverer 2.1 with the SEQUEST HT search algorithm against NCBI refseq database for Oryctolagus cuniculus. Dynamic modifications were set for the carbamidomethylation of cysteine [+57.02 Da], oxidation of methionine [+15.99 Da], N-terminal TMT 6 plex [+229.16 Da], and TMT labeling of lysine [+229.16 Da]. MS/MS spectra were searched with a precursor mass tolerance of 10 ppm and a fragment mass tolerance of 0.36 Da. Trypsin was specified as the protease with a maximum number of missed cleavages set to 2. A false discovery rate was calculated using PERCOLATOR (Käll et al., 2007) and was set at <1% to score high confidence peptide identifications. Normalized reporter intensities were imported into Perseus 1.5.5.3 for statistical analyses (Cox and Mann, 2012). Functional and pathway analysis was performed using DAVID Bioinformatics Resources 6.8 (Huang et al., 2009a,b). Validation of proteomic data Select ANOVA significant (p ≤ .05) protein markers were validated by enzyme-linked immunosorbent assay (ELISA). Due to limited sample volumes, equal volumes of each time point were pooled for all 6 rabbits for analysis by ELISA. Creatine kinase M-type (CKM) (Cloud-Clone Corporation, Katy, Texas) and reactive oxygen species (ROS) assay (Cell BioLabs, Inc., San Diego, California) were performed according to the manufacturer’s directions. All assays were read using a Bio-Tek Synergy HT (Winooski, Vermont) spectrophotometer using Gen5 2.07 software. Metabolomics Sample preparation A 100 µl of thawed plasma was mixed with 820 µl of extraction solution containing isotopically labelled internal standard (ISTD) mixture. The extraction solution was made fresh in sufficient quantities to perform extraction of all samples at once. It was composed of 800 parts of a precipitation solution (8:1:1 acetonitrile: methanol: acetone) and 20 parts of the ISTD mixture (working stock). The ISTD mixture is prepared by making working stocks of each solution at 2 mg/ml by dissolving 10 mg of each standard in 5 ml of 90:10 water: acetonitrile. A working stock solution was prepared by combining the following volumes of each ISTD stock into a single vial containing 4715 µl of Fisher Optima gold label water with 0.1% FA (final volume 5000 µl): d3-creatine (10 µl), d10-leucine (10 µl), d3-l-tryptophan (10 µl), 13C6-citric acid (20 µl), 13C11-tryptophan (100 µl), 13C6-leucine (10 µl), 13C6-l-phenylalanine (10 µl), T-BOC-l-tert-leucine (10 µl), and T-BOC-l-aspartic acid (5 µl). Upon addition of the extraction solution, each sample is vortexed and stored at 4°C for 30 min to complete protein precipitation. Each sample is centrifuged at 20 000 × g for 10 min at 4°C to pellet precipitate. A 750 µl of supernatant is transferred to a new tube taking care not to disturb the protein pellet. Each sample is dried to completeness and stored at −80°C until LC-MS analysis. Liquid chromatography mass spectrometry analysis (LC-MS) Immediately prior to LC-MS analysis each fraction was reconstituted in 100 µl of Fisher Optima gold label H2O with 0.1% FA containing a final concentration of 1 µg/ml of d5-carfentanil (Cerilliant, Round Rock, Texas) and vortexed briefly. Samples were placed in the refrigerator for 10–15 min to allow for resuspension. Finally, each sample is centrifuged at 20 000 × g for 10 min and transferred into glass autosampler vials (Agilent, Santa Clara, California) for analysis. Each sample was analyzed on a Thermo Fisher Orbitrap Q Exactive Plus mass spectrometer coupled to a Thermo Fisher Ultimate 3000 RSLCnano system. Injections of each sample (2 µl) were resolved with the loading pump (350 µl/min) on a 100 µm × 2.1 mm id ACE Excel 2 C18-PFP (Mac-Mod Analytical) using a 22.5 min flow gradient [0→3 min at 100% A, ramp from 3 → 13 min at 20% A/80% B, hold 13 → 16 min at 20% A/80% B, return to initial conditions 16 → 20 min, hold 20 → 22.5 min at 100% A, curve = 5]. For the flow gradient, the A buffer is water with 0.1% formic acid and the B buffer is 100% acetonitrile. Orbitrap MS1 scans were acquired with a resolution of 70 000 with a scan range of m/z 70–1000. AGC target was set to 3E6 with a maximum injection time of 100 ms. All metabolomics data were acquired in positive and negative ionization mode using the heated electrospray ionization source (HEIS). The source settings were as follows: spray voltage: ±3.7 kV, capillary temperature: 325°C, sheath gas (N2): 30 arbitrary units (AU), auxiliary gas (N2): 10 AU, and the probe heater: 350°C. Data processing Raw spectral files were converted to mzXML using MSConvert from ProteoWizard (Kessner et al., 2008) and processed using MZmine 2.3 (Pluskal et al., 2010). For each data file, peaks were extracted, deconvoluted, and deisotoped. Alignment was conducted utilizing join aligner with a 10 ppm tolerance for m/z values and 0.2 min tolerance for retention times. Peak finder was utilized to gap fill missing peaks. The resulting features list was compared against an in-house generated feature library for retention time and m/z matches. The data sets in the form of peak intensity tables were then imported into Metaboanalyst (Version 4, www.metaboanalyst.ca) for multivariate statistical analyzes. Data were filtered using interquartile ranges, normalized by sum, log transformed, and auto scaled for principal component analysis (PCA), partial least squares–discriminant analysis (PLS-DA), heat map cluster analysis, and one-way ANOVA testing. Additional data processing, including KEGG pathway analysis, was performed by Compound Discoverer 2.0 (Thermo Fisher Scientific, San Jose, California). Metabolites were considered statistically significant if they exhibited a 2-fold change or greater and p value of <.05. RESULTS Proteomic Analysis A total of 1388 proteins were identified and quantified with high confidence, by at least 2 peptides and 1 protein-unique peptide which belonged to 787 unique protein groups. For these proteins groups, 327 proteins were observed in at least 6 of the 9 time points in this study. Protein quantitation was performed on these proteins by normalizing to the total ion chromatogram (TIC) across all experimental data and scaled to 100 to calculate relative abundance. PCA showed that the global expression of the proteins identified in this study cluster together by animal and not by time post-exposure (Figure 2A). Figure 2. View largeDownload slide A, PCA of the 327 proteins observed in at least two-thirds of the time points. The proteins cluster by animal, rather than by time point. B, Hierarchical clustering analysis of the 327 proteins shows that the proteins cluster by time post-exposure on the x-axis. On the y-axis, the HCA shows the proteins cluster into three main subsets: down-regulated over the time points (pink), up-regulated over the time points (coral), and up-regulated for a period of time and returning to baseline around day 13 (tan). Figure 2. View largeDownload slide A, PCA of the 327 proteins observed in at least two-thirds of the time points. The proteins cluster by animal, rather than by time point. B, Hierarchical clustering analysis of the 327 proteins shows that the proteins cluster by time post-exposure on the x-axis. On the y-axis, the HCA shows the proteins cluster into three main subsets: down-regulated over the time points (pink), up-regulated over the time points (coral), and up-regulated for a period of time and returning to baseline around day 13 (tan). The reported ratios were then averaged across the 6 animals by time post-exposure. Z-score based hierarchical cluster analysis (HCA) of these proteins and the collection time points set can be seen in Figure 2B. HCA clustering shows that the data loosely clusters by time post-exposure. This is evident as the immediate time points (control, 6 h, and 1D), early time points (2D and 3D), intermediate time points (6D and 7D), and late time points (8D and 9D) cluster together. Interestingly, in the analysis, the latest time point, 13D clusters most closely to the immediate time points. The HCA shows that the proteins cluster (>4 proteins) into 3 unique subsets: (1-pink) down-regulated over time, (2-coral) up-regulated over time, and (3-tan) up-regulated for a period of time and starts returning to normal around day 13. Each cluster was functionally interpreted using the KEGG_PATHWAY feature in DAVID. The first cluster (pink), had statistically significant hits (p-value <.05) for multiple pathways involved with metabolism. Pathway analysis of the second cluster (coral) revealed that 35% of the proteins are involved in the complement and coagulation cascades. This particular pathway had a p-value of 1.1 × 10−35. The final and largest cluster also revealed the most statistically significant hits in the complement/coagulation cascade (p = 9.9 × 10−20) as well as the proteasome pathway (p = 7.8 × 10−12). Of the set of 327 proteins, 43 were found to be significantly different due to expression changes that were 2-fold or higher. ANOVA statistical analysis was also performed and identified 19 proteins that were statistically significant (p-value < .05) following carfentanil exposure. Table 1 lists the subset of statistically significant proteins and how their expression profiles change over the time course when compared with their pre-exposure level. A total of 5 proteins were convincingly up-regulated over the course of the 13 days. Interestingly, 4 of these proteins were various fibrinogen isoforms, with the other being coagulation factor XIII A chain. Eight other proteins were down-regulated at some point throughout the duration of the experiment. Seven of these proteins were immediately down-regulated at the 6 h time point after exposure and remained suppressed throughout the duration of the study. Haptoglobin (Hp) was the only protein that was immediately suppressed following carfentanil exposure but returned and stayed at baseline 24 h post-exposure. Most notable were 2 proteins, CKM and serum amyloid A-1 (SAA), that were immediately elevated following intoxication, peaking at 1D and then slowly returning to baseline 6 days post-exposure. Table 1. Proteins with ±2-Fold Expression Change AND ANOVA Significant Table 1. Proteins with ±2-Fold Expression Change AND ANOVA Significant Because ANOVA statistics were applied to the proteomics data set, it is possible that a single protein expression anomaly at a given time point or within one animal could preclude a protein marker. As such, we also analyzed the expression profiles of each animal for the 29 proteins that exhibited expression changes ±2-fold or higher (Table 2) that were not identified during the ANOVA analysis. However, after closely looking at these potential markers we observed that 89% of these targets, 26 in total, had a single data point on animal #2 on 6D that was saliently up regulated when the rest of the animals in the study showed significant suppression. The other 3 proteins that did not pass the threshold were β-enolase, retinal dehydrogenase, and von Willebrand factor. Both retinal dehydrogenase and von Willebrand factor had <2 time point anomalies for at least 2 of the animals. Table 2. Proteins with ±2-Fold Expression Change but NOT ANOVA Significant Table 2. Proteins with ±2-Fold Expression Change but NOT ANOVA Significant Considering the entire data set, the protein markers for carfentanil exposure seem to largely fit within 3 physiological categories which include: (1) oxidative damage, (2) cardiac damage/coagulation, and (3) metabolism. The expression distribution of select proteins grouped by general function are depicted in Figure 3. The expression of select proteins linked to ROS (thioredoxin, peroxiredoxin-1 [PRDX1], and peroxiredoxin-6 [PRDX6]) were reduced on average 2-fold throughout the duration of the study starting at day 3 post-exposure. For all 3 markers, animal #2 appeared to be an outlier which was most notable at 6D. Select cardiac and coagulation markers include β-enolase, fibrinogen alpha chain, and CKM. β-enolase and CKM showed a significant increase within the first 6 h post-exposure across all animals, peaking at 24 h. There was a fairly large standard deviation at the 24 h time point for β-enolase, which is likely the cause for it getting excluded during the statistical analysis. Multiple fibrinogen markers were significantly increased immediately following intoxication and remained significantly above baseline throughout. Metabolism-linked protein markers (glyceraldehyde-3-phosphate dehydrogenase [GAPDH], and l-lactate dehydrogenase B chain X1) were shown to be suppressed. There is a large degree of variability at the early time points, up to 2D but was much more consistently suppressed (>2-fold) by 3D. As discussed previously, animal #2 for all markers was much higher at 6D than all other animals and time points. Phosphatidylethanolamine-binding protein 1, a protein shown to directly bind opioids (Grandy et al., 1990), was also suppressed in all animals beginning at 2D. Suppression of this protein was observed out to 13D. As seen with the other markers, animal #2 at 6D is perceived to be an outlier. Figure 3. View largeDownload slide Expression profiles of selected proteins graphed for each individual animal. Asterisk (*) indicates proteins that were ANOVA significant. Figure 3. View largeDownload slide Expression profiles of selected proteins graphed for each individual animal. Asterisk (*) indicates proteins that were ANOVA significant. Select Protein Marker Validation Due to limited sample volumes, it was only possible to validate a small subset of markers using orthogonal non-MS approaches. Additionally, we also had to pool samples, at equal volumes, at like time points in order to have enough plasma to perform the validation assays. Regardless, we did not have enough sample to perform the analysis for the full-time course. As the goal of this study was to diagnose exposure to carfentanil as soon as possible, we first chose to validate CKM, as the proteomics analysis showed its expression to be most up-regulated at 6 h. As shown in Figure 4A, CKM was shown to be up-regulated and peaked at the 6 h time point at nearly 1.9-fold by ELISA. The expression tapered down but was still above baseline at the latest time point tested (1D). Although the fold change is not the same magnitude as the proteomics data, the expression profile is conserved across both experimental approaches. Because multiple proteins were seen in the study that are linked to ROS, we quantitatively measured the levels of free hydrogen peroxide (Figure 4B). Surprisingly, hydrogen peroxide levels never deviated from baseline throughout the study. Figure 4. View largeDownload slide Validation assays for (A) CKM and (B) free hydrogen peroxide. Figure 4. View largeDownload slide Validation assays for (A) CKM and (B) free hydrogen peroxide. Metabolomic Analysis Untargeted metabolomics was performed on the plasma samples to augment the proteomic analysis. In this study, we identified 5258 unique features; 3152 and 2106 in positive and negative ionization modes respectively. In total, 98 unique features were identified by accurate mass and retention time through comparison with our in-house curated library. Via PCA analysis, Figure 5, the data separates between the group of pre-exposure, post-exposure and 6 h time points and the time points from 1D through 13D regardless of ionization mode. A vast majority of the unique features (71%) were identified in the positive mode analysis; therefore, only this data was used for an in-depth quantitative analysis. The unique spectral features were further filtered so that only the log 2-fold change and ANOVA significant metabolites remained, and that down-selection yielded over 500 metabolites. A very select number of those metabolites and their pathways are shown in Table 3. Table 3. Select Metabolites with ±2-Fold Expression Change AND ANOVA Significant Table 3. Select Metabolites with ±2-Fold Expression Change AND ANOVA Significant Figure 5. View largeDownload slide PCA for (A) positive and (B) negative mode metabolomics analysis. Figure 5. View largeDownload slide PCA for (A) positive and (B) negative mode metabolomics analysis. Interestingly, we observed a number of statistically significant features that were identified in the linoleic acid, arachidonic acid, and glutathione metabolism pathways. Figure 6 depicts box-and-whisker plots of selected metabolites representing these pathways. Figures 6A–C show metabolites from the linoleic acid metabolism pathway that are significantly up-regulated 24 h after carfentanil exposure and stay up-regulated through 13D. Figures 6D–F show metabolites from the arachidonic acid metabolism and glutathione metabolism pathways that are down-regulated 6 h post-exposure (Figs. 6D and 6E) and 24 h post-exposure (Figure 6F). These stay down-regulated through 13D. The relationship between Figures 6A–C and 6D and 6E is particularly interesting, as part of the linoleic acid metabolism pathway feeds into the arachidonic acid (Figure 7). The metabolites that are up-regulated in the linoleic acid pathway are in the direction away from the arachidonic acid, implying that the arachidonic acid metabolism pathway is disrupted. Figure 6. View largeDownload slide Box-and-whiskers plots of (A) 13S-HODE, (B) 13-OxoODE, (C) 9, 12, 13-TriHOME, (D) prostaglandin A2, (E) prostaglandin E2, and (F) 5-Oxo-proline. Figure 6. View largeDownload slide Box-and-whiskers plots of (A) 13S-HODE, (B) 13-OxoODE, (C) 9, 12, 13-TriHOME, (D) prostaglandin A2, (E) prostaglandin E2, and (F) 5-Oxo-proline. Figure 7. View largeDownload slide A portion of the linoleic acid metabolism pathway. All boxes indicate features that were identified; log 2-fold increase in expression is indicated in red, and p-value significant features are in purple. Figure 7. View largeDownload slide A portion of the linoleic acid metabolism pathway. All boxes indicate features that were identified; log 2-fold increase in expression is indicated in red, and p-value significant features are in purple. DISCUSSION Presently, much of what we know about carfentanil is based upon our understanding of fentanyl and related analogs used in the clinic. These synthetic opioids, similar to their natural opiate counterparts, elicit their therapeutic responses by targeting central mu (µ) opioid receptors (Wax et al., 2003). Central depression of nociceptive pathways results in desirable therapeutic endpoints such as analgesia, sedation, euphoria, and other anxiolytic effects (Ling et al., 1983; Shook et al., 1990). Adverse effects such as respiratory depression (if prolonged, apnea), constipation, nausea, cough suppression, and chest wall rigidity can also commonly occur (Borison, 1989). Prolonged apnea alone, as well as central depression in combination with vomiting, can lead to injury or fatality. It is common for investigators whose interest is to study multiple doses or time points post-exposure to acquire biosamples at the time the animal is sacrificed. Primarily due to throughput issues, many laboratories utilize a sample pooling strategy to minimize the number of samples analyzed by LC-MS/MS for a given study. In unrelated studies, our laboratory and others have observed that this approach leads to a great degree of variability which ultimately confounds the results (Liu et al., 2015). In order to control the inter-animal variability, our study was designed so each animal was processed separately, using that individual’s pre-exposed sample as a control. Confirming the literature, despite these animals being of the same sex, of similar age, and their environmental conditions tightly controlled, there was still a great deal of animal to animal variability. This is best depicted in the PCA plot show in Figure 2A as the animals loosely cluster together independent of the time post-exposure. Based upon perturbations of the plasma proteome in this study, we have identified 4 classes of proteins which seem to be impacted. This includes markers that suggest that: (1) cardiac damage, (2) activation of complement and coagulation pathways, (3) generation of ROS, and (4) disruption of normal energy metabolism all occurred following carfentanil exposure. Additionally, we identified decreased levels of phosphatidylethanolamine-binding protein, a protein known to directly bind opioids and promote G protein-coupled signaling (Grandy et al., 1990; Kroslak et al., 2001). Known markers for a heart attack (troponin, myoglobin, and creatine kinase MB) have not been correlated with exposures to low levels of opioids (Odum and Young, 2018; Saunders et al., 2009). In agreement with the literature, we also did not identify these classical protein markers; however, we did observe a statistically significant increase in CKM. Given creatine kinase’s history as a cardiac biomarker, this target was also validated by a traditional ELISA (Figure 4A). In addition to CKM, we also identified β-enolase as a punitive early marker of exposure. In 1987, Nomura et al. reported that β-enolase is a specific marker for acute myocardial infarction but not for other cardiovascular diseases (Nomura et al., 1987). Another early marker that increased by 6 h was SAA. SAA followed similar kinetics as β-enolase, peaking by 24 h. A group out of the University of Melbourne-Australia identified SAA as a biomarker for acute exacerbations of chronic obstructive pulmonary disease (COPD) (Bozinovski et al., 2008). Levels of Hp also rapidly changed following exposure to the toxicant; however, its expression was suppressed instead of increased. A recent proteomics study also identified Hp as a prognostic marker for patients with myocardial infarction (Haas et al., 2011). Multiple fibrinogen isoforms (alpha, beta, gamma chain X1, fibrinogen-like protein 1) were also found to rapidly increase and stay elevated throughout the study following exposure to carfentanil. Interestingly, elevated fibrinogen levels have been linked to the development of stroke and myocardial infarction (Wilhelmsen et al., 1984). Taken together these protein markers for carfentanil exposure suggest that there was a disruption in breathing and likely a myocardial infarction event that took place despite an absence of observable signs and symptoms. The generation of ROS due to morphine and fentanyl exposure have been reported previously (Schattauer et al., 2017; Skrabalova et al., 2013). In this study, we identified multiple proteins linked to ROS and all were found to be suppressed 2-fold or greater throughout the duration of the study. Of these proteins which include catalase, PRDX1 and PRDX6, thioredoxin was the only protein that passed the ANOVA statistical test and showed a significant suppression as early as 6 h post-exposure. Perhaps the most compelling validation of this proteomic data set is the direct link of ROS to PRDX6 which was published in 2017. Schattauer et al. convincingly demonstrated that upon agonist stimulation of the µ opioid receptor, PRDX6 is recruited to the receptor complex by c-Jun N-terminal kinase (JNK) phosphorylation (Schattauer et al., 2017). PRDX6 is then activated resulting in the release of ROS via NADPH oxidase and the subsequent inactivation of the µ opioid receptor. PRDX6 has been implicated in a wide range of diseases and molecular functions including lipid and fatty acid metabolism (Fisher 2018; Schmitt et al., 2015). Its expression could be inhibited due to a variety of reasons including transcription, translation, or protein turnover. More work is needed to explain the suppression observed in this study; however, this work provides the first evidence that PRDX1 may also be involved in µ opioid receptor signaling pathway. Out of the 327 proteins that were quantitatively assessed in the proteome, 2 of the 3 clusters (Figure 2, tan and pink) were highly enriched for proteins linked to coagulation and complement using the DAVID bioinformatics resource. This includes: von Willebrand factor (up late), thrombospondin (down), fibrinogens (multiple isoforms) (up), coagulation factor XIII A-chain (up), and fibronectin (up). All of these proteins were observed to be increased during the study except for thrombospondin. There have been multiple reports in the literature which indicate that exposure to fentanyl and fentanyl analogs, especially via inhalation can cause alveolar hemorrhage (Cole et al., 2015; Ruzycki et al., 2016). Outside of these select reports, there is very little in the literature linking opioids to vascular dysregulation. Respiratory depression can result in decreased blood pressure which could lead to vascular damage. On the other hand, there is a significant body of literature linking NADPH oxidase (NOX) to increased vasoconstriction. Given PRDX6 role in the activation of this enzyme, it is possible that vascular dysregulation occurs following carfentanil exposure explaining the elevation of multiple proteins involved in the coagulation cascade. Based upon the proteomics data, it is clear that the host’s metabolism is also significantly impacted upon carfentanil exposure. GAPDH, an important enzyme for glycolysis, was suppressed approximately 2-fold 3 days post-exposure. GAPDH has also recently been implicated in transcriptional regulation and apoptosis. Oxygen deprivation following opioid exposure causes an immediate biochemical shift in a subject to generate energy via alternative mechanisms. l-lactate dehydrogenase B chain isoform X1 is an enzyme which is critical for one of these major pathways which converts the product of glycolysis, pyruvate, to lactate. Elevation of this enzyme in the blood is known marker for a wide range of diseases including myocardial infarction and cancer (Galen et al., 1975; Schwartz, 1992). The generation of ROS and the shift in expression of the proteins with enzymatic properties observed in the study have been convincingly linked to changes in the metabolome. In fact, based upon the PCA plots, the metabolome (Figure 5) appears to be more heavily impacted than the proteome (Figure 2A). However, from this high level comparison, it is hard to draw this conclusion as the proteome data set is not nearly as rich in “features” as the metabolomics data set due to the protein dynamic range issues associated with blood plasma proteomics. Regardless, the proteomics data strongly links to the metabolomics data via the PRDX family of proteins. Upon activation, the PRDX proteins can result in the generation of ROS at the cellular lipid membrane are heavily involved in the oxidation of linoleic acid. The perturbation of PRDX6 causes disruptions in linoleic acid metabolism (Fisher, 2018). The directionality of the interruption, points to more oxidized fatty acids being produced through lipoxygenases (LOX) and fewer metabolites feeding into arachidonic acid metabolism (Fruehauf and Meyskens, 2007; Ostuni et al., 2010). Arachidonic acid metabolism is a crucial part of PRDX6-dependent proliferation and lipid signaling (Schmitt et al., 2015). Literature also points to correlation between LOX activation and NOX stimulation, which mediates the generation of ROS (Cho et al., 2011; Kumar et al., 2009; Mahipal et al., 2007). As previously stated, PRDX6 also stimulates NOX, indicating ROS generation from multiple signaling cascades. Additionally, the catalytic mechanism for PRDX6 activity depends on a singly conserved cysteine residue and utilizes glutathione as a cofactor for peroxidase activity (Rhee et al., 2005; Schmitt et al., 2015). The disruption of PRDX6 causes disarray in both cysteine and methionine metabolism and glutathione metabolism. Metabolites in all of these pathways are significantly up-regulated in the 24 h following exposure to carfentanil and these metabolites stay up-regulated through 13D. Due to the disruption of the linoleic acid pathway, lipidomic analysis of opioid-exposed biofluids may also yield fruitful exposure markers. In conclusion, several protein and metabolite markers have been found to be significantly changed over a 13-day period following exposure to low doses of carfentanil. These proteins can be linked to cardiac damage, coagulation cascades, generation of ROS, and disruption of energy metabolism. CKM and β-enolase were identified as markers for myocardial infarction. Although there were several proteins which could be linked to the metabolic changes, PRDX6’s role in lipid metabolism was the most compelling. In addition to proteomic changes, perturbations in the host’s metabolism were also observed. This was expected, but the magnitude, speed, and duration of change was surprising. Two metabolic pathways, linoleic and arachidonic acid, were dysregulated in opposite directions suggesting that a single biochemical molecule could be to blame. Collectively, these protein and metabolite alterations lend themselves to the development of better diagnostic strategies for exposure to these ultra-potent opioid compounds when the toxicant itself is not easily detectable. ACKNOWLEDGMENTS This research was performed while E.M.M. held NRC Research Fellowship Awards at the Edgewood Chemical Biological Center. Conclusions and opinions presented here are those of the authors and are not the official policy of the U.S. Army, ECBC, or the U.S. Government. Information in this report is cleared for public release and distribution is unlimited. FUNDING This work was supported by the Chem-Bio Diagnostics program contract (CB3810) from the Department of Defense Chemical and Biological Defense program through the Defense Threat Reduction Agency (DTRA) to J.W.S and T.G. DTRA is a Combat Support Agency and a Defense Agency with a 3-pronged mission: (1) to counter the threats posed by the full spectrum of weapons of mass destruction (WMD), including chemical, biological, radiological, nuclear, and high-yield explosives; (2) counter the threats posed by the growing, evolving categories of improvised threats, including improvised explosive devices, car bombs and weaponized consumer drones, as well as the tactics, technologies and networks that put them on the battlefield; and (3) ensure the U.S. military maintains a safe, secure, effective, and credible nuclear weapons deterrent. REFERENCES Borison H. L. ( 1989 ). Area postrema—chemoreceptor circumventricular organ of the medulla-oblongata . Prog. Neurobiol. 32 , 351 – 390 . Google Scholar Crossref Search ADS PubMed Bozinovski S. , Hutchinson A. , Thompson M. , MacGregor L. , Black J. , Giannakis E. , Karlsson A.-S. , Silvestrini R. , Smallwood D. , Vlahos R. , et al. . ( 2008 ). Serum amyloid A is a biomarker of acute exacerbations of chronic obstructive pulmonary disease . Am. J. Resp. Crit. Care 177 , 269 – 278 . Google Scholar Crossref Search ADS Casale J. F. , Mallette J. R. , Guest E. M. ( 2017 ). Analysis of illicit carfentanil: Emergence of the death dragon . Forensic Chem. 3 , 74 – 80 . Google Scholar Crossref Search ADS Cho K.-J. , Seo J.-M. , Kim J.-H. ( 2011 ). Bioactive lipoxygenase metabolites stimulation of NADPH oxidases and reactive oxygen species . Mol. Cells 32 , 1 – 5 . Google Scholar Crossref Search ADS PubMed Cole J. B. , Dunbar J. F. , McIntire S. A. , Regelmann W. E. , Slusher T. M. ( 2015 ). Butyrfentanyl overdose resulting in diffuse alveolar hemorrhage . Pediatrics 135 , e740 – e743 . Google Scholar Crossref Search ADS PubMed Cox J. , Mann M. ( 2012 ). 1D and 2D annotation enrichment: A statistical method integrating quantitative proteomics with complementary high-throughput data . BMC Bioinformatics 13 , S12. Google Scholar Crossref Search ADS PubMed Dolnik A. , Pilch R. ( 2003 ). The Moscow theater hostage crisis: The perpetrators, their tactics, and the Russian response . Int. Negotiation 8 , 577 – 611 . Google Scholar Crossref Search ADS Doucette C. ( 2017 ). Carfentanil: The drug of mass destruction. Toronto Sun. Available at: https://torontosun.com/news/local-news/carfentanil-the-drug-of-mass-destruction. Accessed August 1, 2018. Fisher A. B. ( 2018 ). The phosphlipase A2 activity of peroxiredoxin 6 . J. Lipid Res . 59 , 1132 – 1147 . Google Scholar Crossref Search ADS PubMed Fruehauf J. P. , Meyskens F. L. ( 2007 ). Reactive oxygen species: A breath of life or death? Clin. Cancer Res . 13 , 789 – 794 . Google Scholar Crossref Search ADS PubMed Galen R. S. , Reiffel J. A. , Gambino S. R. ( 1975 ). Diagnosis of acute myocardial infarction: Relative efficiency of serum enzyme and isoenzyme measurements . JAMA 232 , 145 – 147 . Google Scholar Crossref Search ADS PubMed Grandy D. K. , Hanneman E. , Bunzow J. , Shih M. , Machida C. A. , Bidlack J. M. , Civelli O. ( 1990 ). Purification, cloning, and tissue distribution of a 23-kDa rat protein isolated by morphine affinity chromatography . Mol. Endocrinol . 4 , 1370 – 1376 . Google Scholar Crossref Search ADS PubMed Haas B. , Serchi T. , Wagner D. R. , Gilson G. , Planchon S. , Renaut J. , Hoffmann L. , Bohn T. , Devaux Y. ( 2011 ). Proteomic analysis of plasma samples from patients with acute myocardial infarction identifies haptoglobin as a potential prognostic biomarker . J. Proteomics 75 , 229 – 236 . Google Scholar Crossref Search ADS PubMed Huang D. W. , Sherman B. T. , Lempicki R. A. ( 2009a ). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists . Nucleic Acids Res . 37 , 1 – 13 . Google Scholar Crossref Search ADS Huang D. W. , Sherman B. T. , Lempicki R. A. ( 2009b ). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources . Nat. Protoc . 4 , 44. Google Scholar Crossref Search ADS Käll L. , Canterbury J. D. , Weston J. , Noble W. S. , MacCoss M. J. ( 2007 ). Semi-supervised learning for peptide identification from shotgun proteomics datasets . Nat. Methods 4 , 923. Google Scholar Crossref Search ADS PubMed Keshishian H. , Burgess M. W. , Gillette M. A. , Mertins P. , Clauser K. R. , Mani D. , Kuhn E. W. , Farrell L. A. , Gerszten R. E. , Carr S. A. ( 2015 ). Multiplexed, quantitative workflow for sensitive biomarker discovery in plasma yields novel candidates for early myocardial injury . Mol Cell Proteomics M114 , 046813 . Kessner D. , Chambers M. , Burke R. , Agus D. , Mallick P. ( 2008 ). ProteoWizard: Open source software for rapid proteomics tools development . Bioinformatics 24 , 2534 – 2536 . Google Scholar Crossref Search ADS PubMed Krechetnikov A. ( 2012 ). Moscow theatre siege: Questions remain unanswered . BBC News [serial online]. Available at: http://www.bbc.com/news/world-europe-20067384. Accessed May 13, 2015. Kroslak T. , Koch T. , Kahl E. , Höllt V. ( 2001 ). Human phosphatidylethanolamine-binding protein facilitates heterotrimeric G protein dependent signaling . J. Biol. Chem . 276 , 39772 – 39778 . Google Scholar Crossref Search ADS PubMed Kumar K. A. , Arunasree K. M. , Roy K. R. , Reddy N. P. , Aparna A. , Reddy G. V. , Reddanna P. ( 2009 ). Effects of (15S)‐hydroperoxyeicosatetraenoic acid and (15S)‐hydroxyeicosatetraenoic acid on the acute‐lymphoblastic‐leukaemia cell line Jurkat: Activation of the Fas‐mediated death pathway . Biotechnol. Appl. Bioc . 52 , 121 – 133 . Google Scholar Crossref Search ADS Kutukova N. ( 2003 ). Zamoskvoretskiy Court shall continue the examination of complaints of victims in “Nord-Ost” on the Moscow prosecutor's office . Zaprava [serial online]. Available at: http://feedbackgroup.info/proetcon/sudnordost.htm. Accessed March 9, 2017. Leary S. L. , Underwood W. , Anthony R. , Gwaltney-Brant S. , Poison A. , Meyer R. ( 2013 ). AVMA Guidelines for the Euthanasia of Animals: 2013 Edition . American Veterinary Medical Association , Schaumburg, IL . Ling G. S. F. , Spiegel K. , Nishimura S. L. , Pasternak G. W. ( 1983 ). Dissociation of morphine's analgesic and respiratory depressant actions . Eur. J. Pharmacol. 86 , 487 – 488 . Google Scholar Crossref Search ADS PubMed Liu Y. , Buil A. , Collins B. C. , Gillet L. C. , Blum L. C. , Cheng L.-Y. , Vitek O. , Mouritsen J. , Lachance G. , Spector T. D. , et al. . ( 2015 ). Quantitative variability of 342 plasma proteins in a human twin population . Mol. Syst. Biol . 11 , 786. Google Scholar Crossref Search ADS PubMed Mahipal S. V. , Subhashini J. , Reddy M. C. , Reddy M. M. , Anilkumar K. , Roy K. R. , Reddy G. V. , Reddanna P. ( 2007 ). Effect of 15-lipoxygenase metabolites, 15-(S)-HPETE and 15-(S)-HETE on chronic myelogenous leukemia cell line K-562: Reactive oxygen species (ROS) mediate caspase-dependent apoptosis . Biochem. Pharmacol . 74 , 202 – 214 . Google Scholar Crossref Search ADS PubMed National Research Council . ( 2010 ). Guide for the Care and Use of Laboratory Animals. National Academies Press , Washington, DC . Nomura M. , Kato K. , Nagasaka A. , Shiga Y. , Miyagi Y. , Fukui R. , Nakano H. , Abo Y. , Okajima S. , Nakai A. ( 1987 ). Serum beta-enolase in acute myocardial infarction . Heart 58 , 29 – 33 . Google Scholar Crossref Search ADS Odum E. P. , Young E. E. ( 2018 ). Elevated cardiac troponin I, creatine kinase and myoglobin and their relationship with cardiovascular risk factors in patients with type 2 diabetes . Diabetes Metab. Syndr. 12 , 141 – 145 . Google Scholar Crossref Search ADS PubMed Ostuni M. A. , Gelinotte M. , Bizouarn T. , Baciou L. , Houée-Levin C. ( 2010 ). Targeting NADPH-oxidase by reactive oxygen species reveals an initial sensitive step in the assembly process . Free Radic. Biol. Med . 49 , 900 – 907 . Google Scholar Crossref Search ADS PubMed Papsun D. , Isenschmid D. , Logan B. K. ( 2017 ). Observed carfentanil concentrations in 355 blood specimens from forensic investigations . J. Anal. Toxicol . 41 , 777 – 778 . Google Scholar Crossref Search ADS PubMed Philp M. , Fu S. ( 2018 ). A review of chemical ‘spot’tests: A presumptive illicit drug identification technique . Drug Test. Anal . 10 , 95 – 108 . Google Scholar Crossref Search ADS PubMed Pluskal T. , Castillo S. , Villar-Briones A. , Oresic M. ( 2010 ). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data . BMC Bioinformatics 11 , 395. Google Scholar Crossref Search ADS PubMed Rhee S. G. , Chae H. Z. , Kim K. ( 2005 ). Peroxiredoxins: A historical overview and speculative preview of novel mechanisms and emerging concepts in cell signaling . Free Radic. Biol. Med . 38 , 1543 – 1552 . Google Scholar Crossref Search ADS PubMed Riches J. R. , Read R. W. , Black R. M. , Cooper N. J. , Timperley C. M. ( 2012 ). Analysis of clothing and urine from Moscow theatre siege casualties reveals carfentanil and remifentanil use . J. Anal. Toxicol. 36 , 647 – 656 . Google Scholar Crossref Search ADS PubMed Ruzycki S. , Yarema M. , Dunham M. , Sadrzadeh H. , Tremblay A. ( 2016 ). Intranasal fentanyl intoxication leading to diffuse alveolar hemorrhage . J. Med. Toxicol . 12 , 185 – 188 . Google Scholar Crossref Search ADS PubMed Saunders A. B. , Hanzlicek A. S. , Martinez E. A. , Stickney M. J. , Steiner J. M. , Suchodolski J. S. , Fosgate G. T. ( 2009 ). Assessment of cardiac troponin I and C‐reactive protein concentrations associated with anesthetic protocols using sevoflurane or a combination of fentanyl, midazolam, and sevoflurane in dogs . Vet. Anaesth. Analg . 36 , 449 – 456 . Google Scholar Crossref Search ADS PubMed Schattauer S. S. , Land B. B. , Reichard K. L. , Abraham A. D. , Burgeno L. M. , Kuhar J. R. , Phillips P. E. , Ong S. E. , Chavkin C. ( 2017 ). Peroxiredoxin 6 mediates Gαi protein-coupled receptor inactivation by cJun kinase . Nat. Commun . 8 , 743 . Google Scholar Crossref Search ADS PubMed Schmitt A. , Schmitz W. , Hufnagel A. , Schartl M. , Meierjohann S. ( 2015 ). Peroxiredoxin 6 triggers melanoma cell growth by increasing arachidonic acid dependent lipid signaling . Biochem. J . 471 , 267 – 279 . Google Scholar Crossref Search ADS PubMed Schwartz M. K. ( 1992 ). Enzymes as prognostic markers and therapeutic indicators in patients with cancer . Clin. Chim. Acta 206 , 77 – 82 . Google Scholar Crossref Search ADS PubMed Seth P. , Scholl L. , Rudd R. A. , Bacon S. ( 2018 ). Overdose deaths involving opioids, cocaine, and psychostimulants—United States, 2015–2016 . Am. J. Transplant. 18 , 1556 – 1568 . Google Scholar Crossref Search ADS PubMed Shook J. E. , Watkins W. D. , Camporesi E. M. ( 1990 ). Differential roles of opioid receptors in respiration, respiratory-disease, and opiate-induced respiratory depression . Am. Rev. Respir. Dis. 142 , 895 – 909 . Google Scholar Crossref Search ADS PubMed Skrabalova J. , Drastichova Z. , Novotny J. ( 2013 ). Morphine as a potential oxidative stress-causing agent . Mini-Rev. Org. Chem . 10 , 367 – 372 . Google Scholar Crossref Search ADS PubMed Tavernise S. , Kishkovsky S. ( 2002 ). Hostage drama in Moscow: The scene; the survivors dribble out, all with a story to tell. The New York Times [serial online]. Available at: http://www.nytimes.com/2002/10/28/world/hostage-drama-moscow-scene-survivors-dribble-all-with-story-tell.html. Accessed 1 August 2018. Thevis M. , Geyer H. , Bahr D. , Schanzer W. ( 2005 ). Identification of fentanyl, alfentanil, sufentanil, remifentanil and their major metabolites in human urine by liquid chromatography/tandem mass spectrometry for doping control purposes . Eur. J. Mass Spectrom. 11 , 419 – 427 . Google Scholar Crossref Search ADS Tran B. Q. , Miller P. R. , Taylor R. M. , Boyd G. , Mach P. M. , Rosenzweig C. N. , Baca J. T. , Polsky R. , Glaros T. ( 2018 ). Proteomic characterization of dermal interstitial fluid extracted using a novel microneedle-assisted technique . J. Proteome Res. 17 , 479 – 485 . Google Scholar Crossref Search ADS PubMed Van Nimmen N. F. J. , Poels K. L. C. , Veulemans H. A. F. ( 2004 ). Highly sensitive gas chromatographic-mass spectrometric screening method for the determination of picogram levels of fentanyl, sufentanil and alfentanil and their major metabolites in urine of opioid exposed workers . J. Chromatogr. B Anal. Technol. Biomed. Life Sci . 804 , 375 – 387 . Google Scholar Crossref Search ADS Vanbever W. F. M. , Niemegeers C. J. E. , Schellekens K. H. L. , Janssen P. A. J. ( 1976 ). N-4-Substituted 1-(2-arylethyl)-4-piperidinyl-N-phenylpropanamides, a novel series of extremely potent analgesics with unusually high safety margin . Drug Res . 26 , 1548 – 1551 . Wang L. Q. , Bernert J. T. ( 2006 ). Analysis of 13 fentanils, including sufentanil and carfentanil, in human urine by liquid chromatography-atmospheric-pressure ionization-tandem mass spectrometry . J. Anal. Toxicol. 30 , 335 – 341 . Google Scholar Crossref Search ADS PubMed Wang Y. , Yang F. , Gritsenko M. A. , Wang Y. , Clauss T. , Liu T. , Shen Y. , Monroe M. E. , Lopez-Ferrer D. , Reno T. , et al. . ( 2011 ). Reversed‐phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells . Proteomics 11 , 2019 – 2026 . Google Scholar Crossref Search ADS PubMed Wax P. M. , Becker C. E. , Curry S. C. ( 2003 ). Unexpected “gas” casualties in Moscow: A medical toxicology perspective . Ann. Emerg. Med . 41 , 700 – 705 . Google Scholar Crossref Search ADS PubMed Wilhelmsen L. , Svärdsudd K. , Korsan-Bengtsen K. , Larsson B. , Welin L. , Tibblin G. ( 1984 ). Fibrinogen as a risk factor for stroke and myocardial infarction . N. Engl. J. Med . 311 , 501 – 505 . Google Scholar Crossref Search ADS PubMed Zhao H. , Du H. , Liu M. , Gao S. , Li N. , Chao Y. , Li R. , Chen W. , Lou Z. , Dong X. ( 2018 ). Integrative proteomics–metabolomics strategy for pathological mechanism of vascular depression mouse model . J. Proteome Res. 17 , 656 – 669 . Google Scholar Crossref Search ADS PubMed Published by Oxford University Press on behalf of the Society of Toxicology 2018. This work is written by US Government employees and is in the public domain in the US.
TI - Proteomic and Metabolomic Profiling Identify Plasma Biomarkers for Exposure to Ultra-low Levels of Carfentanil
JF - Toxicological Sciences
DO - 10.1093/toxsci/kfy259
DA - 2019-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/proteomic-and-metabolomic-profiling-identify-plasma-biomarkers-for-6OXfTkKWbn
SP - 524
VL - 167
IS - 2
DP - DeepDyve
ER -