Systematic Metabolomic Analysis of Eicosanoids after Omega-3

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Systematic Metabolomic Analysis of Eicosanoids after Omega‑3 Polyunsaturated Fatty Acid Supplementation by a Highly Specific Liquid Chromatography−Tandem Mass Spectrometry-Based Method Xu Zhang,†,‡,∥ Nan Yang,§,∥ Ding Ai,‡ and Yi Zhu*,‡,§ †

Collaborative Innovation Center of Tianjin for Medical Epigenetics, Research Center of Basic Medical Sciences, and ‡Department of Physiology and Pathophysiology, Tianjin Medical University, Tianjin 300070, China § Department of Physiology and Pathophysiology, Peking University Health Science Center, Beijing 100191, China S Supporting Information *

ABSTRACT: Omega-3 (ω-3) polyunsaturated fatty acids (PUFAs) have beneficial effects in many pathological processes, especially cardiovascular disease, and their protective eicosanoid metabolites are thought to play important roles. However, how ω-3 PUFAs affect the eicosanoid profile has not been elucidated comprehensively. Here, we systematically analyzed the eicosanoid metabolites induced by ω-3 PUFA supplementation. We developed an LC−MS/MS-based method covering 32 arachidonic acid (ARA) metabolites and 37 ω-3 PUFA-derived products. The limits of detection for eicosanoids were between 0.0625 and 1 pg and the detection specificity was optimized. We then quantified eicosanoids in mouse and human plasma and mouse aorta samples after ω-3 PUFA supplementation. Levels of EPA hydroxyl products, 4-HDoHE, 17,18-EEQ, 17,18-DiHETE, TXB2, and LXA4 were significantly changed in both mouse samples, and those of 2-series PGs, EDPs and DHA hydroxyl products were changed in aorta samples. Correlation network analysis of mouse plasma data revealed that some eicosanoids had higher connection degree or betweenness centrality score than others after ω-3 PUFA supplementation. Eicosanoids in human plasma were profiled across five time points after ω-3 PUFA supplementation. Fuzzy c-mean clustering algorithm suggested that the time curves of eicosanoid activity could be described with three kinetic patterns: sustained upregulation, short-term upregulation, and downregulation. This is the first systematic profiling of eicosanoids with ω-3 PUFA supplementation. The highly specific eicosanoid metabolomic and related data analysis methods would be powerful tools for comprehensive eicosanoid study. KEYWORDS: eicosanoids, omega-3 polyunsaturated fatty acid, metabolomics, quantitative profiling, network analysis, dynamic analysis

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in cardiovascular diseases.2 As the major components of ω-3 PUFAs, eicosapentaenoic acid (EPA, C20:5) and docosahexaenoic acid (DHA, C22:6) share a similar structure and the same enzymatic system with ARA. 3-series PGs and TXs, 5series LTs and LXs, hydroxyeicosapentaenoic acids (HEPE), epoxyeicosatetraenoic acids (EEQ), and dihydroxyeicosatetraenoic acids (diHETE) are derived from EPA. Hydroxydocosahexaenoic acids (HDoHE), epoxydocosapentaenoic acids (EDP), and dihydroxydocosapentaenoic acids (DiHDPA) are derived from DHA.3 In addition, a new genus of specialized pro-resolving mediators (SPM), including E- and D-series resolvins (RvE and RvD), (neuro)protectins (PD), and maresins (MaR), could be biosynthesized from EPA and DHA.4

INTRODUCTION Eicosanoids are a series of bioactive lipid molecules metabolized from polyunsaturated fatty acids (PUFA). Omega-6 (ω-6) PUFA arachidonic acid (ARA, C20:4) is the major precursor of eicosanoids. Eicosanoids are substrates for oxidation via three primary enzymatic pathways by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450s (CYP) and are autoxidized in a nonenzymatic manner. Classic eicosanoids contain 2-series prostaglandins (PG) and thromboxanes (TX), 4-series leukotrienes (LT) and lipoxins (LX), hydroxyeicosatetraenoic acids (HETE), epoxyeicosatrienoic acids (EET), and their corresponding dihydroxyeicosatrienoic acid (DHET) products.1 Many ARA-derived eicosanoids are pro-inflammatory and may be involved in some inflammation-related diseases such as cancer, autoimmune diseases, and cardiovascular diseases. Different from ω-6 fatty acids, omega-3 (ω-3) PUFAs are considered protective in many pathological processes, especially © XXXX American Chemical Society

Received: November 23, 2014

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DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research Although ω-3 and ω-6 PUFAs are structurally similar, they are functionally distinct and their metabolites often have opposing physiologic functions. For example, ω-3 PUFAderived PGs and LTs are thought to have similar but weaker pro-inflammatory effects than ARA-derived metabolites.5 In contrast, CYP450 epoxy metabolites of EEQ and EDP usually have stronger vasorelaxation and anti-inflammation activities than their ARA analogues EET.6 As well, SPMs can be synthesized to contribute to the resolution process.4,7 Because of competition in enzymes, supplementation with ω-3 PUFAs might shift eicosanoid metabolism, which implies an indirect effect of ω-3 PUFAs on the pathological status via the effect on ARAs. Thus, study of the regulation of eicosanoids by ω-3 PUFAs would help in understanding their protective effects. However, how ω-3 PUFAs affect the eicosanoid profile has not been systematically studied. Liquid chromatography−tandem mass spectrometry (LC− MS/MS)-based targeted metabolomics is the only effective strategy to solve this problem. Several groups have developed some eicosanoid metabolomic methods,8 with attempts made to elucidate the eicosanoid profile induced by ω-3 PUFAs; however, researchers focused on eicosanoids in only one or two pathways9,10 and did not examine the relations among them in depth. Here, we introduce an eicosanoid metabolomic method that can quantify 32 ARA and 37 ω-3 PUFA metabolites (covering most of the common ones in the three main metabolic pathways) and use it to study biological specimens from mice and humans with ω-3 PUFA supplementation. Because the protective effect of ω-3 PUFAs was mostly reported in the cardiovascular system, we examined mouse plasma and aorta samples. In light of the existence of isomeric and isobaric structures, we focused on the multiple reaction monitoring (MRM) ion transition design and LC separation to ensure detection specificity, which was balanced against sensitivity, quantitation accuracy, and analysis time. We used multivariate statistics such as principal component analysis (PCA) and hierarchical clustering analysis to profile the eicosanoid metabolome. Because eicosanoids are metabolized in a network, the levels of some eicosanoids are related to others. We used correlation network analysis to study the relationships between eicosanoids. We also attempted to describe the degree of importance of the eicosanoids by virtue of network theory. To investigate the effect of ω-3 PUFAs on the human eicosanoid profile, we analyzed plasma from 12 healthy volunteers who took fish oil, an EPA/DHA supplement, across five time points. We provide a dynamic description of each eicosanoid, to reflect the characteristics of each eicosanoid and provide a metabolic baseline for human subjects taking ω-3 PUFA supplementation and how it works in humans.

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and formic acid of HPLC grade or better were from Fisher Scientific (Pittsburgh, PA). Oasis HLB 10 mg SPE cartridges were from Waters Co. (Milford, MA). Centrifuge tube filters were from Corning Co. (Corning, NY). All other chemical reagents were from Sigma (St. Louis, MO). Sample Preparation

Plasma was extracted by solid-phase extraction (SPE). Before extraction, Waters Oasis-HLB cartridges were washed with methanol (1 mL) and Milli-Q water (1 mL). Samples were spiked with IS mixture (5 ng for each) and loaded onto cartridges. Cartridges were washed with 1 mL of 5% methanol. The aqueous plug was pulled from the SPE cartridges under high vacuum, and SPE cartridges were further dried under high vacuum for 20 min. Analytes were eluted into tubes with 1 mL of methanol. The eluent was then evaporated to dryness. Mouse aortas were extracted with liquid−liquid extraction. Tissues were washed with phosphate-buffered saline and blotted dry with filter paper. After being weighed, tissues were homogenized with 500 μL of methanol (2% formic acid and 0.01 mol/L BHT) spiked with IS mixture (5 ng for each). Samples were mixed on a vortexer for 5 min. After centrifugation (12000 g for 10 min at 4 °C), the supernatant was transferred to a new tube. Water (700 μL) and ethyl acetate (1 mL) were added to the supernatant. The sample was mixed vigorously for 2 min and centrifuged for 10 min at 12000 g. The upper organic phase was transferred to a new tube and the water phase was extracted again. The organic phase was combined and then evaporated to dryness. The dried residue was dissolved in 100 μL of 30% acetonitrile. After vigorous mixing, samples were filtered by use of centrifuge tube filters (nylon membrane, 0.22 μm). Ultraperformance Liquid Chromatography

Chromatographic separations involved use of an UPLC BEH C18 column (1.7 μm, 100 × 2.1 mm i.d.) consisting of ethylene-bridged hybrid particles (Waters, Milford, MA). The column was maintained at 25 °C and the injection volume was set to 10 μL. Solvent A was water and solvent B was acetonitrile. The mobile-phase flow rate was 0.6 mL/min. Chromatography was optimized to separate 32 ARA metabolites in 9 min. The gradient was 0−1.5 min from 30% to 40% B;1.5−6.5 min to 60% B; 6.5−7.6 min to 80% B, which was maintained for 1 min; and 8.6−8.8 min reduced to 30% B and maintained for 0.2 min. The same gradient was applied to 37 ω3 PUFA metabolites. Mass Spectrometry

Targeted profiling of ARA and ω-3 PUFA metabolites involved use of a 5500 QTRAP hybrid triple quadrupole linear ion trap mass spectrometer (AB Sciex, Foster City, CA) equipped with a turbo ion spray electrospray ionization source. The mass spectrometer was operated with Analyst 1.5.1 software. Analytes were detected by MRM scans in negative mode. The dwell time used for all MRM experiments was 25 ms. The ion source parameters were CUR = 40 psi, GS1 = 30 psi, GS2 = 30 psi, IS = −4500 V, CAD = MEDIUM, and TEMP = 500 °C.

EXPERIMENTAL SECTION

Reagents

We purchased AA, EPA, DPA, DHA, and corresponding metabolite standard chemicals and isotopic internal standards (IS), including 6-keto-PGF1α-d4, PGE2-d4, LTB4-d4, 11,12DHET-d11, 20-HETE-d6, 5-HETE-d8, 8,9-EET-d11, ARA-d8, EPA-d5, and DHA-d5, from Cayman Chemical Co. (Ann Arbor, MI). Butylated hydroxytoluene (BHT) was from Sigma− Aldrich Inc. (St. Louis, MO). Acetonitrile (LC−MS grade) and methanol [high-performance liquid chromatography (HPLC) grade] were from Merck (Darmstadt, Germany). Ethyl acetate

Animals

All animal experimental protocols were approved by Peking University Institutional Animal Care and Use Committee. The investigation conformed to the Guide for the Care and Use of Laboratory Animals by the U.S. National Institutes of Health (NIH Publication, 8th ed., 2011). Six-week-old C57BL/6 mice were kept in a 12-h light/dark cycle at a controlled room B

DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Table 1. Mass Spectrometry Parameters, Limit of Detection, and Quantification Performance in Analysis of Eicosanoidsa analyte 6-keto-PGF1a TXB2 PGF2a PGE2 PGD2 LXA4 PGJ2 PGB2 LTB4 14,15-DHET 11,12-DHET 8,9-DHET 5,6-DHET 15-deoxy-PGJ2 19-HETE 20-HETE 18-HETE 17-HETE 16-HETE 15-HETE 11-HETE 15-oxo-ETE 12-HETE 8-HETE 9-HETE 5-HETE 14,15-EET 5-oxo-ETE 11,12-EET 8,9-EET 5,6-EET PGB3 PGD3 PGE3 PGF3a TXB3 resolvin-D1 resolvin-D2 5,6-diHETE 14,15-diHETE 17,18-diHETE maresin-1 16,17-EDP 19,20-EDP 4-HDoHE 7-HDoHE 8-HDoHE 10-HDoHE 11-HDoHE 13-HDoHE 14-HDoHE 16-HDoHE 17-HDoHE 20-HDoHE 5-HEPE 8-HEPE 9-HEPE 11-HEPE 12-HEPE 15-HEPE 18-HEPE

MRM transition

IS

DP

EP

CE

CXP

LODb (pg)

range (pg)

linearity (R)

→ → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → →

6-keto-PGF1a-d4 PGE2-d4 PGE2-d4 PGE2-d4 PGE2-d4 LTB4-d4 PGE2-d4 PGE2-d4 LTB4-d4 11,12-DHET-d11 11,12-DHET-d11 11,12-DHET-d11 11,12-DHET-d11 PGE2-d4 20-HETE-d6 20-HETE-d6 20-HETE-d6 20-HETE-d6 20-HETE-d6 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 8,9-EET-d11 8,9-EET-d11 8,9-EET-d11 8,9-EET-d11 8,9-EET-d11 PGE2-d4 PGE2-d4 PGE2-d4 PGE2-d4 PGE2-d4 LTB4-d4 LTB4-d4 11,12-DHET-d11 11,12-DHET-d11 11,12-DHET-d11 LTB4-d4 8,9-EET-d11 8,9-EET-d11 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8 5-HETE-d8

−60 −60 −60 −60 −60 −60 −50 −52 −52 −60 −60 −60 −60 −60 −58 −75 −60 −60 −60 −70 −70 −70 −70 −70 −70 −70 −58 −58 −58 −58 −58 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60 −60

−10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10 −10

−36 −24 −28 −24 −24 −20 −22 −27 −21 −24 −28 −30 −26 −18 −21 −25 −23 −22 −19 −17 −22 −23 −20 −19 −21 −19 −16 −22 −17 −17 −16 −19 −19 −20 −30 −24 −26 −30 −25 −23 −22 −23 −15 −16 −21 −19 −19 −21 −19 −19 −19 −19 −18 −19 −19 −18 −19 −20 −19 −17 −16

−10 −18 −24 −26 −24 −10 −16 −10 −19 −10 −10 −10 −10 −24 −10 −22 −18 −21 −21 −22 −14 −11 −16 −16 −14 −15 −18 −18 −16 −14 −17 −19 −20 −18 −16 −16 −16 −14 −13 −19 −24 −17 −24 −24 −10 −12 −11 −12 −10 −18 −14 −20 −18 −22 −10 −14 −20 −18 −18 −22 −20

2 0.25 0.5 2 2 0.0625 1 0. 25 0.25 0.125 0.125 0.25 0.25 0.125 2 2 0.125 0.5 0.25 0.125 0.0625 0.125 0.0625 0.125 0.25 0.125 0.25 0.125 0.125 0.25 0.25 0.5 0.5 1 0.25 0.25 0.5 1 1 1 0.125 0.5 0.125 2 0.5 0.25 1 0.25 0.25 0.0625 0.25 0.125 1 0.25 0.125 0.125 1 0.25 0.25 0.25 1

4−2000 0.5−2000 1−2000 4−400 4−400 0.125−2000 2−2000 0. 5−400 0.5−400 0.5−400 0.5−400 0.5−2000 0.5−2000 0.25−80 4−2000 4−2000 0.5−400 1−400 0.5−400 0.25−400 0.125−80 0.25−400 0.125−2000 0.25−2000 0.5−400 0.25−2000 0.5−2000 0.25−2000 0.25−400 0.5−2000 0.5−400 1−2000 1−400 2−2000 0.5−400 0.5−2000 1−400 2−400 2−400 2−400 0.25−400 1−400 0.25−400 4−400 1−80 0.5−400 2−400 0.5−400 0.5−400 0.125−400 0.5−400 0.25−400 2−400 0.5−400 0.25−400 0.25−400 2−400 0.5−400 0.5−400 0.5−400 2−400

0.9991 0.9994 0.9989 0.9995 0.9997 0.9999 0.9985 0.9996 0.9990 0.9963 0.9971 0.9964 0.9982 0.9893 0.9901 0.9971 0.9999 0.9994 0.9997 0.9988 0.9997 0.9994 0.9998 0.9992 0.9904 0.9973 0.9963 0.9999 0.9995 0.9924 0.9977 0.9995 0.9993 0.9995 0.9989 0.9998 0.9992 0.9987 0.9985 0.9987 0.9994 0.9984 0.9997 0.999 0.9992 0.9996 0.9990 0.9997 0.9991 0.9996 0.9990 0.9998 0.9970 0.9986 0.9986 0.9990 0.9993 0.9995 0.9998 0.9996 0.9990

369 369 353 351 351 351 333 333 335 337 337 337 337 315 319 319 319 319 319 319 319 317 319 319 319 319 319 317 319 319 319 331 349 349 351 367 375 375 335 335 335 359 343 343 343 343 343 343 343 343 343 343 343 343 317 317 317 317 317 317 317

163 169 309 271 271 115 189 235 195 207 167 127 145 271 231 289 261 247 233 219 167 113 179 155 151 115 219 129 167 155 191 269 269 269 193 169 215 175 145 207 247 177 274 299 101 141 109 153 121 193 205 233 245 241 115 155 149 167 179 219 259

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DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research Table 1. continued analyte

MRM transition

8,9-EEQ 11,12-EEQ 14,15-EEQ 17,18-EEQ ARA EPA DPA DHA

317 317 317 317 303 301 329 327

→ → → → → → → →

123 179 207 215 259 257 231 229

6-keto-PGF1a-d4 PGE2-d4 LTB4-d4 11,12-DHET-d11 20-HETE-d6 8,9-EET-d11 5-HETE-d8 ARA-d8 EPA-d5 DHA-d5

373 355 339 348 325 330 327 311 306 332

→ → → → → → → → → →

167 319 197 167 281 155 309 267 262 288

IS 8,9-EET-d11 8,9-EET-d11 8,9-EET-d11 8,9-EET-d11 ARA-d8 EPA-d5 DHA-d5 DHA-d5

DP

EP

CE

CXP

LODb (pg)

range (pg)

linearity (R)

−60 −60 −60 −60 −58 −60 −60 −60

−10 −10 −10 −10 −10 −10 −10 −10

−20 −17 −17 −18 −21 −16 −22 −19

−16 −18 −19 −21 −11 −22 −25 −24

1 0.25 0.125 0.5 8 0.125 0.25 8

2−400 0.5−400 0.25−2000 1−400 8−2000 0.25−400 0.5−2000 8−2000

0.9990 0.9998 0.9992 0.9982 0.9985 0.9995 0.9984 0.9997

−70 −60 −60 −60 −60 −58 −70 −58 −60 −60

−10 −10 −10 −10 −10 −10 −10 −10 −10 −10

−36 −18 −23 −26 −23 −19 −16 −21 −18 −17

−18 −28 −18 −17 −21 −11 −14 −11 −20 −23

a

MS parameters were optimized individually for each eicosanoid. Deuterated internal standards were assigned to eicosanoids according to the structures. MRM, multiple reaction monitoring; DP, declustering potential; EP, entrance potential; CE, collision energy; CXP, collision cell exit potential; LOD, limit of detection. bLOD was defined as the minimal concentration on the column with signal-to-noise ratio >3.

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temperature and fed a high ω-3 PUFA diet (containing 0.5% EPA and 0.35% DHA, w/w) or control normal diet for 3 weeks. Blood was collected via cardiac puncture and transferred into tubes containing sodium ethylenediaminetetraacetate (NaEDTA, 1.2 mg/mL) and BHT (0.01 mol/L). Blood was drawn and moved gently to prevent hemolysis. Plasma was isolated by centrifugation at 3000 rpm at room temperature for 3 min. Plasma samples were extracted within 2 h after collection. Aorta samples were collected from animals at the time of death and stored at −80 °C until extraction.

RESULTS AND DISCUSSION

Development of a Highly Specific Eicosanoid Metabolomic Method

Because of the high structural similarity among eicosanoid species, distinguishing each eicosanoid by chromatographic retention times or MRM ion pairs alone is difficult. For example, PGE2 and PGD2 are isomeric, and the only difference between them is the reversed site of the keto and hydroxyl groups. These two PGs produce the exact same ion fragmentation pattern on MS. The MRM ion transition used is not specific for them, so a complete baseline chromatographic separation is needed. In another situation, the LOX products 8-HETE and 12-HETE have similar hydrophobicity, so reverse-phase column-based chromatography cannot resolve the two molecules well. However, they produce specific ion fragments (8-HETE m/z 319 → 155, 12-HETE m/z 319 → 179),14 which could differentiate them. For any eicosanoid analyzed by this method, we considered chromatographic separation and MRM ion transitions comprehensively for high specificity. This strategy gave our method an advantage in detection specificity. On the basis of the principle mentioned above, we optimized the MS and LC parameters for high sensitivity. For MS parameters, declustering potential (DP), entrance potential (EP), collision energy (CE), and collision cell exit potential (CXP) were optimized for each analyte’s MRM ion transition (Table 1). Acquisition dwell time was optimized to 25 ms to ensure the signal-to-noise ratio for each analyte.15 For LC parameters, the flow speed and mobile phase composition were optimized for a sharp chromatography peak. The flow speed was set to 0.6 mL/min, at which the UPLC column used can acquire its highest theoretical plate numbers and achieve balanced sensitivity and resolution. Under this condition, the column pressure was from 3300 psi (80% acetonitrile, ACN) to 7900 psi (30% ACN), which was lower than the maximum pressure limitation (15 000 psi). Because the objective analytes were detected in negative ion mode, we avoided the common

Human Subjects

We collected the blood of 12 healthy volunteers (6 male, 6 female, average age 24 ± 2) before and after fish oil supplementation with capsules (Nutrifynn caps, 2 g/day, containing 360−400 mg of EPA and 240−280 mg of DHA).To assess the dynamics of the eicosanoid system, each analyte was quantified five times (0, 3, 7, 14, and 21 days). Blood was drawn into tubes containing NaEDTA. Plasma was isolated as mentioned above. BHT (0.01 mol/L) was added to each plasma sample. Plasma samples were extracted within 2 h after collection. All subjects have been provided written consent in this study, and the study protocol was approved by the local ethics committees (Peking University Health Science Center, China) and the procedures committees according to the Declaration of Helsinki and Good Clinical Practice guidelines. Data Analysis

Raw data processing involved use of Analyst 1.5.1. PCA, hierarchical clustering analysis, and Fuzzy c-means (FCM) clustering involved use of R 3.0.3 (with the packages muma, GMD11 and Mfuzz12). The correlation network was constructed with use of Cytoscape 3.1.1.13 D

DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Figure 1. Chromatograph of standard mixture of eicosanoids analyzed by the eicosanoid metabolomic method. (A, B) Total ion current chromatographs of (A) 32 arachidonic acid (ARA)-derived metabolites and (B) 37 ω-3 polyunsaturated fatty acids (PUFA)-derived metabolites, analyzed in two separate runs. (C−E) Metabolites with the same multiple reaction monitoring (MRM) transition were separated completely. (F−M) Metabolites with similar structures were detected by specific MRM transitions.

Figure 2. Quantification of performance and quality control of the eicosanoid metabolomic method. (A) Heat map representing the correlation coefficients (R value) of the fitted quantitation standard curve. (B) Heat maps showing the accuracy and precision of the method and the stability of each eicosanoid; the data were acquired by use of a quality control sample at four different concentrations.

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DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Figure 3. Eicosanoid metabolomic analysis of mouse plasma and aorta samples after ω-3 PUFA induction. (A) Principal component analysis (PCA) score plot and (B) loading plot for data from mouse plasma. (C) Score plot and (D) loading plot for data from mouse aorta. (E, F) Heat map showed eicosanoid profiling in plasma and aorta samples. (G, H) Volcano plot screening out eicosanoids with significant change in level (x-fold change > 2 or 0.99 (most >0.995; Figure 2A). The LOD of each metabolite was assessed by the standard curve data and was determined as the smallest detected concentration with signal-to-noise ratio (S/N) > 3 (shown in Table 1). Accuracy and precision were determined by standard mixture quality control (QC) samples prepared at four concentrations (0.1, 1, 10, and 100 ng/mL) spanning the concentration range of the method. Each concentration was injected 3 times. Accuracy was calculated as the averaged percentage difference between the QC and the expected value. Precision was calculated as the relative SD of these three values. The accuracy and precision for each analyte are listed in Table S1, Supporting Information, and exhibited as a heat map (Figure 2B). For most of the eicosanoids, the relative error of accuracy and precision was 0.5 or < 0.5). Positive and negative correlations are represented by black and blue edges, respectively. The yellow−red color scale represents the connective degree of nodes. The node size represents its betweenness centrality.

Figure 5. Eicosanoid metabolomic analysis of human plasma with ω-3 PUFA induction. (A−C) Temporal profiles of eicosanoids assigned to three clusters by Fuzzy c-means (FCM) clustering. The y axis was standardized. Colors of the lines represent the membership level for each eicosanoid calculated by FCM. (D) Volcano plot screening out eicosanoids with changed level (x-fold change >2 or 0.5) and some were negative (PCC < 0.5). The correlations with PCC > 0.5 or < 0.5 were extracted and used for constructing a network (Figure 4B). The black edges represent positive correlations and the blue ones represent H

DOI: 10.1021/pr501200u J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research Table 2. Eicosanoids Assigned in Each Dynamic Curve Cluster after ω-3 PUFA Supplementationa cluster 1

cluster 2

cluster 3

eicosanoid

membership valueb

eicosanoid

membership value

eicosanoid

membership value

17,18-diHETE 14,15-diHETE 8,9-diHETE 11,12-diHETE 19,20-EDP 14,15-EEQ 9-HEPE 16,17-EDP maresin 8-HEPE 18-HEPE 14,15-DHET 17,18-EEQ 20-HDoHE

0.98 0.98 0.97 0.96 0.96 0.95 0.95 0.93 0.92 0.90 0.89 0.86 0.85 0.82

8-HDoHE 11-HDoHE 7-HDoHE 17-HDoHE 4-HDoHE 11-HEPE 13-HDoHE 14-HDoHE

0.88 0.87 0.87 0.85 0.84 0.84 0.82 0.82

8,9-EET 9-HETE 11,12-EET TXB2 ARA 19-HETE

0.94 0.92 0.90 0.85 0.84 0.82

a

Clustering analysis was conducted with the Fuzzy c-mean algorithm. bValue represents similarity between the eicosanoid dynamic curve and the cluster to which it was assigned.

analyte was quantified across five times (0, 3, 7, 14, and 21 days). The eicosanoid metabolomics data of human plasma are listed in Table S5, Supporting Information. To search for patterns in time curves of the eicosanoids, we used an FCM clustering algorithm. In FCM clustering, each data element was assigned a grade of membership for a set of clusters. The time curves for eicosanoids averaged from the 12 volunteers were grouped in three clusters (Figure 5A−C, color represents membership grade). The eicosanoids assigned in each cluster are listed in Table 2 with their corresponding membership values (filtered by the threshold of 0.8). The three clusters represented different metabolic tendencies. In general, eicosanoids assigned to cluster 1 were continuously increased with ω-3 PUFA supplementation. This cluster mainly contained the CYP450 epoxy metabolites derived from EPA and DHA and their corresponding dihydroxy metabolites. The pro-resolving mediator maresin and some monohydroxyl metabolites were also in this cluster, especially those with the hydroxyl site on the double bond near the ω-carbon position, such as 18-HEPE and 20-HDoHE. Eicosanoids grouped in cluster 2 were increased before 7 days, but their levels could not be sustained. These eicosanoids mainly included DHA hydroxyl metabolites. The retrograde conversion of DHA to EPA could restrict the accumulation of DHA,28 which would cause the level of its metabolites to drop. Cluster 3 represented a decreased pattern, in which the eicosanoids were ultimately decreased at 21 days. ARA-derived eicosanoids were mainly in this cluster. The eicosanoids derived from ARA in cluster 3 were increased in level in the early stage of ω-3 PUFA supplementation (before 7 days). ω-3 PUFA may substitute part of the ARA in membranes, which leads to a temporary increased ARA level in plasma cells. The eicosanoids assigned in different clusters would play different roles. Although the span of the time curve was only 3 weeks, the eicosanoids assigned to clusters 1 and 3 were still more likely to reflect longterm effects of ω-3 PUFA. Eicosanoids with significant changes in level at 21 days were visualized by a volcano plot (Figure 5D). Most of the significantly changed eicosanoids were hydroxyl, epoxy, and diol products of EPA. This trait was in accordance with the previous work, which focused on the change of the eicosanoid profile in human plasma16 and lipoproteins29 after 4 weeks of

negative correlations. Node color represents the connective degree: the more connections a node has, the redder the color. Some features of eicosanoid metabolism were described by this network. First, in general, ω-3 PUFA-derived metabolites in this network negatively correlated with ARA-derived ones directly or indirectly. These two classes of eicosanoids were approximately partitioned by the blue edges, with a few exceptions. Second, under ω-3 PUFA supplementation, its metabolites, especially LOX- and CYP-derived ones, were increased collectively, as reflected by the module on the bottom right of the network. Finally, some eicosanoids had higher connective degree, which was considered as an index for measuring node importance in network theory.26,27 The level of these eicosanoids correlated with more neighbors than ordinary eicosanoids. If the level of these eicosanoids was regulated, many neighbors’ level would also be regulated along with them, which suggested that they would be crucial metabolites in the eicosanoid metabolic pathway under ω-3 PUFA supplementation. The ω-3 PUFA-derived nodes had higher weight in terms of connective degree, because they were regulated collectively. Therefore, we introduced betweenness centrality, another concept in graph theory, to find key nodes in a complementary way. Betweenness centrality represents the proportion of shortest paths passing through a node to the total number of shortest paths, which reflects the ability of the node to control the network. In Figure 4B, metabolite betweenness centrality is indicated by the node size. Thus 5-HETE, 11-HETE, 8,9DHET, and 14,15-EET would be more sensitive targets responding to ω-3 PUFA as compared with other ARA metabolites. According to common ideas in -omics works, choosing targets for the next study would be biased toward well-studied ones. Correlation network analysis suggests choosing targets in another way. Eicosanoid Profiling of Plasma from Humans with ω-3 PUFA Supplementation

Eicosanoid enzymes in humans differ from those in mice. To clarify the effect of ω-3 PUFA on the human eicosanoid profile, we analyzed the plasma of 12 healthy volunteers before and after fish oil supplementation with capsules (Nutrifynn caps, 2g/d, containing 360−400 mg of EPA and 240−280 mg of DHA). To assess the dynamics of the eicosanoid system, each I

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Journal of Proteome Research ω-3 PUFA supplementation. A comparison of susceptible eicosanoids in human and mouse samples is shown in Figure 5E. Hydroxyl EPA metabolites and 17,18-EEQ and its diol product 17,18-diHETE were increased in both mouse and human plasma, meaning that these eicosanoids were susceptible to ω-3 PUFA supplementation regardless of species. The eicosanoids not overlapping might result from different relative doses of ω-3 PUFA for human and mouse, different enzyme profiles, or the complex diet of humans. Different ω-3 PUFA basal level could also affect the degree of eicosanoids in response to ω-3 PUFA supplementation.30 Some potent metabolites increased in mouse but not human samples should be considered in translational research of ω-3 PUFA.

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CONCLUSIONS We developed a method to investigate the eicosanoid metabolome. To ensure the detection sensitivity and coverage of major eicosanoid targets, we optimized the specificity and inspected the accuracy and precision of the method. This method was used to investigate the change in profile of eicosanoids after ω-3 PUFA supplementation in mice and humans. This represents the first time the eicosanoid metabolome was studied in the mouse aorta and profiled dynamically in human plasma. The comprehensive data analysis adopted in this work was helpful to clarify the characteristics of eicosanoid metabolism and discover crucial targets regulated by ω-3 PUFA.

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ASSOCIATED CONTENT

S Supporting Information *

Five tables listing quality control of eicosanoid metabolomic method, eicosanoid stability assessment, and eicosanoid profiles of mouse plasma, mouse aorta, and human plasma. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Telephone (86) 22-8333-6665; e-mail [email protected]. Author Contributions ∥

X.Z. and N.Y. contributed equally to this work

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the Major National Basic Research Grant of China (2012CB517504) and the National Natural Science Foundation of China (81130002 and 81420108003).

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REFERENCES

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