Lipid Profiling following Intake of the Omega 3 Fatty Acid DHA Identifies the Peroxidized...

Lipid Profiling following Intake of the Omega 3 FattyAcid DHA Identifies the Peroxidized Metabolites F4-Neuroprostanes as the Best Predictors of AtherosclerosisPreventionCecile Gladine1, John W. Newman2,3, Thierry Durand4, Theresa L. Pedersen2, Jean-Marie Galano4,

Celine Demougeot5, Olivier Berdeaux6,7,8, Estelle Pujos-Guillot1,9, Andrzej Mazur1, Blandine Comte1*

1 UMR1019 Unite de Nutrition Humaine (UNH), INRA, CRNH Auvergne, Clermont Universite, Universite d’Auvergne, Clermont-Ferrand, France, 2 Obesity and Metabolism

Research Unit, USDA, ARS, Western Human Nutrition Research Center, Davis, California, United States of America, 3 Department of Nutrition, University of California, Davis,

California, United States of America, 4 Institut des Biomolecules Max Mousseron (IBMM), UMR CNRS 5247, Universites de Montpellier I et II, France, Montpellier, France,

5 EA 4267 Fonctions et Dysfonctions epitheliales, University of Franche-Comte, Besancon, France, 6 UMR6265 Centre des Sciences du Gout et de l’Alimentation, CNRS,

Dijon, France, 7 UMR1324 Centre des Sciences du Gout et de l’Alimentation, INRA, Dijon, France, 8 UMR Centre des Sciences du Gout et de l’Alimentation, Universite de

Bourgogne, Dijon, France, 9 UMR 1019, Plateforme d’Exploration du Metabolisme, INRA, Clermont-Ferrand, France

Abstract

The anti-atherogenic effects of omega 3 fatty acids, namely eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) arewell recognized but the impact of dietary intake on bioactive lipid mediator profiles remains unclear. Such a profiling effortmay offer novel targets for future studies into the mechanism of action of omega 3 fatty acids. The present study aimed todetermine the impact of DHA supplementation on the profiles of polyunsaturated fatty acids (PUFA) oxygenatedmetabolites and to investigate their contribution to atherosclerosis prevention. A special emphasis was given to the non-enzymatic metabolites knowing the high susceptibility of DHA to free radical-mediated peroxidation and the increasedoxidative stress associated with plaque formation. Atherosclerosis prone mice (LDLR2/2) received increasing doses of DHA(0, 0.1, 1 or 2% of energy) during 20 weeks leading to a dose-dependent reduction of atherosclerosis (R2 = 0.97, p = 0.02),triglyceridemia (R2 = 0.97, p = 0.01) and cholesterolemia (R2 = 0.96, p,0.01). Targeted lipidomic analyses revealed that boththe profiles of EPA and DHA and their corresponding oxygenated metabolites were substantially modulated in plasma andliver. Notably, the hepatic level of F4-neuroprostanes, a specific class of DHA peroxidized metabolites, was stronglycorrelated with the hepatic DHA level. Moreover, unbiased statistical analysis including correlation analyses, hierarchicalcluster and projection to latent structure discriminate analysis revealed that the hepatic level of F4-neuroprostanes was thevariable most negatively correlated with the plaque extent (p,0.001) and along with plasma EPA-derived diols was animportant mathematical positive predictor of atherosclerosis prevention. Thus, oxygenated n-3 PUFAs, and F4-neuroprostanes in particular, are potential biomarkers of DHA-associated atherosclerosis prevention. While these maycontribute to the anti-atherogenic effects of DHA, further in vitro investigations are needed to confirm such a contentionand to decipher the molecular mechanisms of action.

Citation: Gladine C, Newman JW, Durand T, Pedersen TL, Galano J-M, et al. (2014) Lipid Profiling following Intake of the Omega 3 Fatty Acid DHA Identifies thePeroxidized Metabolites F4-Neuroprostanes as the Best Predictors of Atherosclerosis Prevention. PLoS ONE 9(2): e89393. doi:10.1371/journal.pone.0089393

Editor: Wolf-Hagen Schunck, Max Delbrueck Center for Molecular Medicine, Germany

Received September 12, 2013; Accepted January 20, 2014; Published February 18, 2014

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone forany lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Funding: The project was partially supported by intramural French National Institute for Agricultural Research (INRA, http://www.alimh.inra.fr/) and United StatesDepartment of Agriculture-Agricultural Research Service (USDA-ARS, http://www.ars.usda.gov/main/main.htm), Project 5306-51530-019-00D (JWN). The fundershad no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have delcared that no competing interests exist.

* E-mail: [email protected]

Introduction

Consumption of long chain omega-3 polyunsaturated fatty acids

(LC n-3 PUFAs), notably eicosapentaenoic acid (EPA) and

docosahexaenoic acid (DHA), has been reported to improve the

prognosis of several chronic diseases related to inflammation and

oxidative stress [1,2]. Regarding cardiovascular diseases, protec-

tive effects of LC n-3 PUFAs can be in part ascribed to reduced

athero-thrombotic events [3,4,5]. These are attributable to the

modulation of specific risk factors such as reduction of platelet

aggregation, decrease of plasma triglycerides (TG) and blood

pressure (BP), as well as a direct regulation of systemic and local

inflammation underlying plaque inception, progression and

instability [3,5]. Molecular mechanisms of action of LC n-3

PUFAs have been extensively studied but research gaps remain,

particularly on the identification of the oxygenated metabolites

which are increasingly considered as major effectors of the LC n-3

PUFAs. To date, research has been mainly focusing on the

enzymatic oxygenated metabolites of LC n-3 PUFAs. They

comprise the well-known eicosanoids which are produced from

EPA and involve cyclooxygenase (producing 3-series prostaglan-

dins and thromboxanes) and 5-lipoxygenase (producing 5-series

leukotrienes). An alternative enzymatic pathway involving the 5-

and 15-lipooxygenases and generating resolvins, protectins and

PLOS ONE | www.plosone.org 1 February 2014 | Volume 9 | Issue 2 | e89393

http://www.alimh.inra.fr/

maresins from both EPA and DHA have been more recently

described [6]. Various alcohols, ketones, epoxides and diols can

also be produced from LC n-3 PUFAs by the independent or

coordinated action of lipoxygenases, peroxidases, alcohol dehy-

drogenases, cytochrome P450 epoxygenases and epoxide hydro-

lase [7]. The non-enzymatic pathways also referred to as the free-

radical-mediated peroxidation pathway has been much less

considered as a putative source of bioactive n-3 PUFAs

metabolites. However, LC n-3 PUFAs, and DHA in particular,

are highly prone to free-radical-mediated peroxidation [8] which

produce an array of metabolites from hydroperoxide decomposi-

tion and rearrangement including hydroxyhexenal (HHE) and the

isoprostanes/neuroprostanes (IsoPs/NeuroPs) [9,10,11]. More-

over, the free radical-mediated peroxidation of DHA is likely a

major metabolic pathway during atherogenesis because of the

enhanced production of free radicals in the artery wall [12]. This

emphasizes a conceptual paradox between the atheroprotective

properties of DHA and its susceptibility to peroxidation during

atherogenesis. We thus hypothesized that non-enzymatic oxygen-

ated metabolites generated from DHA could also play a role in

atherosclerosis prevention. To broaden our understanding of

metabolic changes associated with atherosclerosis progression in

the presence and absence of DHA, we designed experiments to ask

two specific questions: 1) What is the impact of DHA supplemen-

tation on the profiles of PUFA oxygenated metabolites? 2) Is there

a relationship between the production of oxygenated metabolites

and the atherosclerotic plaque progression? To address these

questions, we conducted a dose-response intervention study with

DHA in atherosclerosis-prone LDLR2/2 mice and used targeted

lipidomic analyses to quantify PUFA-derived oxygenated metab-

olites in plasma and liver. Multivariate analysis methods including

correlation analyses, hierarchical cluster and projection to latent

structure discriminate analysis (PLS-DA) were finally used to

investigate relationships between plaque extent and the levels of

PUFA oxygenated metabolites. This integrated biological and

biostatistical analysis resulted in oxygenated PUFA metabolite

profiling and the identification of potent predictive variables of

atherosclerosis.

Materials and Methods

Ethics StatementThis study was carried out in strict accordance with the

Institutional Ethics Committee of the INRA. The protocol was

approved by the Committee on the Ethics of Animal Experiments

of the Auvergne Region (Permit Number: CE-2910). All efforts

were made to minimize suffering during the protocol and before

the final experiment.

Mice, Diets and Study OutlineLDLR2/2 mice (n = 120) were purchased from Jackson Labora-

tories (Charles River Laboratories, L’Arbresle, France), housed 10

percage, ina temperaturecontrolledenvironment (2260.8uC)witha

12-h light–dark cycle, and allowed free access to food and water. At 8

weeks of age, mice were given a diet enriched in animal fat (10% of

lard) and low in cholesterol (0.045%, Sigma-Aldrich C75209), and

were randomized into four different groups on the basis of body

weight. In parallel to the diet, mice received by daily oral gavages

(50 mL, 5 days/week) of either oleic acid rich sunflower oil (Lesieur,

Asnieres-sur-Seine, France; Control group) or a mixture of oleic acid

rich sunfloweroil and DHArich tunaoil (OMEGAVIEDHA 90 TG,

Polaris Nutritional Lipids, France containing 90% of DHA as TG).

This oil mixture provided 0.1% (or 1.77 mg/d/mouse), 1% (or

17.7 mg/d/mouse) or 2% (or 35.5 mg/d/mouse) of energy as DHA

(DHA1, DHA2 and DHA3 groups respectively). These 3 doses of

DHA were chosen first to be close to nutritional conditions

(recommended intake for human: 0.1% of energy [13]) and secondly

to levels reported in the literature [14]. Moreover, to avoid oxidation

of oil mixtures, preparations were performed under nitrogen,

aliquoted into 3 mL cryotubes, corresponding to daily doses of

gavage and stored in the dark at 280uC until use. After 20 weeks of

feeding, mice were anaesthetized (40 mg pentobarbital/kg body

weight), blood was drawn into sodium EDTA (200 mM) and plasma

was rapidly collected and stored at 280uC until further utilization.

After rapid washing with sterilized PBS, liver and aorta samples were

immediately frozen into liquid nitrogen and stored at 280uC.

Blood Pressure and Heart rateSystolic blood (sBP) and diastolic blood pressures (dBP) as well

as heart rate were measured in conscious mice using the indirect

tail-cuff method (BP 2000, Visitech System, Apex, North Carolina,

USA). Measurements were performed at the beginning and at the

end of the experimental period.

Quantification of Atherosclerotic LesionsAtherosclerotic lesions were assessed by measuring lipid deposit

in the aortic sinus as previously described [15]. Briefly, four

sections of 10 mm thickness were harvested/slide and 28 slides/

mouse were prepared and stained with Oil red O (Merck,

Darmstadt, Germany) and counterstained with hematoxylin

(Diapath, Martinengo, Italy). Each section was evaluated for Oil

red O staining area under microscope. Image analysis was carried

out using the ImageJ free software (http://rsb.info.nih.gov/ij/) to

quantify the cross-section surface areas of the lesions and the cross-

section surface area of the aorta. The lesion area was calculated by

dividing the surface of the lesion by the surface of the vessel and

expressed as a percentage. This approach allows correcting for

errors caused by oblique sections that may lead to overestimation

of the surface area occupied by a lesion.

Plasma and Liver LipidsPlasma total cholesterol and TG concentrations were deter-

mined as previously described [16]. Liver samples were homog-

enized in NaCl (9 g.l21) with a Polytron homogeniser PT-

MR2100 (Kinermatica AG, Littau/Luzern, Switzerland) and

lipids were extracted by chloroform–methanol (2:1, v/v) under

overnight agitation. The chloroform phase was recovered after

centrifugation and evaporated under dry air. TAGs from the lipid

residue were saponified with 0.5 M KOH–ethanol at 70uC for 30

min followed by the addition of 0.15 M MgSO4 to neutralize the

mixture. After centrifugation (2,0006g; 5 min), glycerol from TAG

in the supernatant was quantified by an enzymatic assay (TG PAP

150 kits, BioMerieux, Marcy-l’Etoile, France). Cholesterol in the

lipid residue was dissolved with isopropanol and measured using

enzymatic commercial kit (Cholesterol RTUTM, BioMerieux,

Marcy-l’Etoile, France).

Plasma Fatty Acids and OxylipinsEsterified plasma lipids were quantified by mass spectrometry

after total lipid extraction in the presence of fatty acid,

cholesterylester, triglyceride, and phospholipid surrogates. Extracts

were split for the independent quantification of esterified fatty

acids and alkali stable oxylipins. Esterified fatty acids were

transformed to fatty acid methyl esters and quantified by GC-

MS. Fractions for oxylipin analyses were enriched with isotopically

labeled oxylipin fatty acids, subjected to base hydrolysis, and the

Lipid Mediators and Atherosclerosis Prevention


http://rsb.info.nih.gov/ij/

resulting free oxylipins were isolated by solid phase extraction

(SPE) and quantified by LC-MS/MS.

Specifically, plasma aliquots (100 mL) were enriched with 5 mL

0.2 mg/ml butylated hydroxytoluene/EDTA in 1:1 methanol:-

water, and a suite of extraction surrogates, which included

deuterated-tri-palmitoyl glycerol (d31-16:0-TG; CDN Isotopes,

Pointe-Claire, Quebec, Canada), deuterated distearoylphosphoti-

dylcholine (d35-18:0-PC; Avanti Polar Lipids, Alabaster, Ala-

bama), dodeca-(9E)-enoyl cholesterylesters (22:1n9-CE; NuChek

Prep, Elysian MN) and dodecatrienoic acid (22:3n3; NuChek

Prep). Lipids were then extracted with 10:8: 11 cylcohexane: 2-

propanol:ammonium acetate as described by Smedes [17]. Briefly,

enriched samples were mixed with cyclopropane/2-propanol,

phases were split with ammonium acetate, the organic phase was

isolated and the aqueous phase was re-extracted with cyclohexane.

The combined organic total lipid extract was reduced to dryness

and reconstituted in 100 mL of 1:1 methanol/toluene.

A sub-aliquot of the total lipid extract was used to quantify

plasma fatty acids as methyl esters by gas chromatography-mass

spectrometry (GC-MS). Extract aliquots (10 mL) were spiked with

15:1n5 free acid to track methylation efficiency and brought to a

final volume of 200 mL with 90:10 methanol/toluene (v/v) and

incubated at 60uC for 1hr with 100 mL 0.5 M sodium methoxide,

followed by 1 hr with 100 mL 0.5 M methanolic HCl. Solutions

were then neutralized with 400 mL 0.25M KHCO3/0.5M K2CO3

and fatty acid methyl esters (FAMEs) were extracted into 400 mL

hexane, and extracts were washed with 400 mL saturated saline. A

60 mL extract aliquot was enriched with 10 mL of 44 mM

tricosanoate methyl ester (23:0; NuChek Prep). A 1 mL injection

was analyzed by GC-MS on an Agilent 6890/5973N with electron

impact ionization and in simultaneous selected ion monitoring/full

scan mode. Analytes were separated on a

30 m60.25 mm60.25 mm DB-225 ms as previously described

[18].

Total alkali stable plasma oxylipins were quantified in total lipid

extracts by UPLC-MS/MS after the isolation of saponified

oxylipins by SPE. Total lipid extract sub-aliquots (40 mL 1:1

methanol/toluene) were enriched with a suite of 12 deuterated

prostanoids, eicosanoids, and octadecanoid free acid surrogates

(Table S1 in File S1), in 5 mL methanol. Enriched aliquots were

incubated at 60uC for 1 hr with 100 mL 0.5 M sodium methoxide,

followed by 1 hr after the addition of 100 mL of water to hydrolyze

methyl esters. Hydrolysis of methyl esters in 1:1 MeOH/water has

been previously shown to efficiently liberate alkali stable methyl

esters [19,20,21] and trans-esterification followed by hydrolysis has

been found to increase yields for lipids with low water solubility

(e.g. CE and TG, data not shown). Oasis HLB SPE columns

(10 mg61cc; Waters Corporation, Milford, MA, USA) were

washed with 1 mL ethyl acetate, 2 mL MeOH, and conditioned

with 2 mL 5% MeOH/0.1% acetic acid SPE prior to sample

extraction. Hydrolyzed samples were up-diluted with 0.5 mL 5%

MeOH/0.1% acetic acid SPE, neutralized with 10 mL 20% glacial

acetic acid, and transferred to SPE reservoirs along with a second

0.5 mL 5% MeOH/0.1% acetic acid rinsate, for a 16% final

organic content by volume. Samples were allowed to extract by

gravity, sorbent phase was washed with 1 mL of 20% MeOH/

0.1% acetic acid and air dried for 30 min at 225 psi. Columns

were then wetted with 0.5 mL MeOH/1% acetic acid and

residues eluted with 1.5 mL of ethyl acetate into 2 mL vials

containing a solution of 6 mL 30% glycerol in methanol. Residues

were brought to glycerol residue under vacuum and reconstituted

with 50 mL MeOH containing 100 nM each of 1-cyclohexylurea-

3-dodecanoic acid (Sigma-Aldrich, St. Louis, MO) and 1-

phenylurea-3-hexanoic acid, as internal standards. The sample

extract was filtered by centrifugation for 3 min at 8uC using

0.1 mm Amicon Ultrafree-135 MC Durapore PVDF filters

(Millipore, Billerica, MA). Five mL of filtered extract was analyzed,

and analytes of interest were separated by reverse phase ultra-

performance liquid chromatograph with a 1.7 mm Acquity BEH

column (Waters, Milford, MA) using a 16 min gradient (Solvent

A = 0.1% acetic acid; Solvent B = 90:10 v/v acetonitrile/isopro-

panol; see Table S2 in File S1 for details). It should be noted, as

showed in Table S1 in File S1, that the apparent surrogate

recoveries were lower than typically observed with these methods,

particularly for the epoxy fatty acids, which may reduce the

accuracy in the final data. However, as replicate precision was

high, the reported data is considered valid for the determination of

the treatment effect investigated in the present study.

Oxylipins were detected on an API 4000 QTrap (AB Sciex,

Framingham, MA, USA) by multiple reaction monitoring (MRM)

after negative mode electrospray ionization, and quantified against

6pt calibration curves using internal standard methodologies.

Analyte retention times, mass transitions, mass spectral parameters

and surrogate/internal standard associations, and estimated

detection limits are presented in Table S3 in File S1. Our

analytical criteria for reporting are as follows: data .3:1 signal to

noise; the relative contribution of background from method blanks

is ,25% of signal; values are within the calibrated range.

It should be noted that alkaline conditions destroy the b-

hydroxy-keto prostanoids (e.g. PGEs, PGDs) and thromboxanes,

as well as cystienyl leukotrienes and ketones (Tables S8, S9, and

S10 in File S1) but not the b-hydroxy-alcohol PGFs [22] or other

oxylipin classes analyzed here (see Table S1 in File S1). While

plasma deuterated PGF2a showed good recoveries and F2-

isoprostanes were detected in ,70% of samples, concentrations

were uniformly low, ,46 the average blank values, and are not

reported here. To provide readers an estimate of the lowest

detectable concentration, the lowest detectable calibrated concen-

tration has been transformed to a plasma equivalent concentration

for each analyte and included in Table S8 in File S1. Finally,

whereas artificial formation of oxylipins during saponification

cannot be completely excluded, the use of EDTA to chelate free

metals which can catalyze auto-oxidative degradation of lipids and

the use of butylated hydroxyl toluene as an anti-oxidant should

have limited this process. Moreover, as shown in Figure S6 in File

S1, the auto-oxidative markers F2-isoprostanes and 9-HETE levels

were low and did not co-vary. Finally, while some oxylipins were

strongly covariant with 9-HETE levels in the samples, others were

not. Importantly, while 9-HETE was correlated with 11-HETE

and 15-HETE concentrations, 12-HETE and plasma epoxide

concentrations were not (Figure S7 in File S1). Taken together

these factors suggest that oxylipins were not formed during sample

processing.

Liver Fatty AcidsTotal lipids were extracted from liver samples following a the

choloroform:methanol procedures of Folch et al. as previously

described [23], and fatty acid methyl esters were prepared for

analysis by gas chromatography-flame ionization detection.

Briefly, after drying with anhydrous sodium sulfate, the organic

extract was evaporated under nitrogen and total lipids dissolved

with toluene/methanol (1/2, v/v). FAMEs were obtained after

trans-esterification followed by acid trans-esterification. GC

analysis of FAMEs was performed using a GC Trace (Thermo

Fischer Scientific, Courtaboeuf, France), equipped with a fused

silica CP-Sil 88 capillary column (100% cyanopropyl-polysiloxane,

100 m60.25 mm60.20 mm; Varian S.A, Les Ulis, France). The

identities of sample methyl esters were determined by comparing



their relative retention times with those of external FAME

standards (Supelco 37 Component Fatty Acid Methyl Esters Mix

and Menhaden Oil; Sigma Aldrich, St Quentin Fallavier, France).

Other standard FAME mixtures were obtained from Nu-Chek-

Prep (Elysian, MN, USA).

Liver HydroxyalkenalsHepatic levels of the hydroxyalkenals thioether protein adducts

(hydroxynonenal or HNE-P and hydroxyhexenal or HHE-P from

n-6 and n-3 PUFA respectively) were measured by GC-MS/MS as

previously described [23]. Briefly, 100 mg of liver tissue were

processed and immediately treated with NaB2H4 to reduce

aldehydes into their chemically stable 2H labeled alcohol

derivatives. Then, proteins were precipitated and resuspended

into 8 mM guanidine buffer. The mixture was spiked with the

labeled internal standard ([2H11] 4-HNE, 0.25 nmol) and incu-

bated with Raney nickel (overnight, 55uC) to cleave the thioether

linkages and reduce the C–C bonds. The free saturated derivatives

were then extracted and derivatized with N-methyl-N-(t-butyldi-

methylsylyl)-trifluoroacetamide (2 hr, 80uC). Samples were ana-

lyzed by GC-MS/MS on a Agilent 6890N GC (Agilent

Technologies, Palo Alto, CA, USA) equipped the a Quattro

Micro tandem mass spectrometer (Waters Corporation, Manche-

ster, UK), as previously described. The system was run in positive

mode with MRM. The collision energies were adjusted to

optimize the signal for the most abundant product (daughter)

ions: m/z 390.258 for [2H]DHN (dihydroxynonene as reduced

HNE), m/z 348.216 for [2H]DHH (dihydroxyhexene as reduced

HHE), and m/z 400.268 for the internal standard [2H11]DHN

using argon as collision gas. Quantification was performed using

calibration curves with external standards. Quantities of HNE-P

and HHE-P adducts reported in this study represent averages of

duplicate sample injections.

Liver F4-neuroprostanesSample preparation was adapted from Musiek et al. (2004) [24].

Briefly samples of liver tissue (10 mg) were homogenized in 2.5 ml

of ice-cold chloroform/methanol (2/1, v/v) containing butylated

hydroxyl toluene (0.004%) and 50 mL of the labeled internal

standard ([2H4]4-F4t, 40 ng/mL). Esterified F4-NeuroPs were

isolated and hydrolyzed by adding 2 ml of 1 M aqueous potassium

hydroxide. The samples were acidified to pH 3 with 1 mL of 1 M

HCl and diluted with 2 mL of 0.1 M formate buffer (pH 3.0). F4-

NeuroPs were then purified by solid phase extraction using an

Oasis HLB extraction cartridge (Waters Corp) as previously

described [25]. Briefly, the cartridge was preconditioned with

methanol and 0.1 M formate buffer (pH 3.0), and washed with

acetonitrile:water (15:85, v:v) followed by heptane. F4-NeuroPs

were eluted by with 2 mL of hexane:ethyl acetate:propan-2-ol

(30:65: 5, v:v:v). Purified F4-NeuroPs were converted to penta-

fluorobenzyl ester/TMS ether derivatives before GC/MS analysis

using the Agilent/Waters GC-MS/MS described above, operating

in negative chemical ionization mode using isobutane as reagent

gas. Injections (2 mL) were performed at 250uC in splitless mode.

The carrier gas was high-purity helium at a constant flow rate of

1 mL/min. Chromatographic separation was performed using an

50 m60.32 mm i.d, 0.25 mm OV-1701 (Ohio Valley Specialty

Co; Marietta, OH, USA) capillary column under the following

conditions: 90uC for 1 min, increased by 40uC/min until 290uC.

The final temperature was held at 290uC for 6 min. MS

parameters were optimized using standard solutions. Maximum

sensitivity was obtained for an ion source temperature set at

120uC, an electron energy at 90 eV and an emission current at

300 mA.

The data were acquired using MRM. The collision energies of

15 eV yielded optimal product ions intensity from the fragmen-

tation of the carboxylate anions: m/z 593.341 for F4-NeuroPs,

and m/z 573.303 for the internal standard [2H4]4-F4t-NeuroP

using argon as collision gas. Quantification was performed using

calibration curves with external standards. The internal standard

[2H4]4-F4t-NeuroP was synthesized according to a recent

published procedure [26,27].

Statistical AnalysesAll data are presented as means 6 SEM. Data following a

Gaussian distribution (test of Kolmogorov & Smirnov) and having

no significant difference between SD (Bartlett’s test) were analyzed

by one-way ANOVA followed by a Tukey-Kramer post-hoc

analysis using GraphPad InStat version 3.06 (GraphPad Software,

San Diego California USA, www.graphpad.com). The other data

were analysed using a non-parametric test (Kruskal Wallis’s test).

Differences were considered significant at p,0.05.

The cumulative results were also subjected to a suite of

multivariate analyses including hierarchical cluster analysis and

PLS-DA. Data treatment prior to multivariate analysis included

missing value imputation, and normality transformation, and

autoscaled i.e. each variable was mean centred and scaled to unit

variance [28]. Where variable data sets were greater than 90%

complete, missing values were computationally imputed using a

probabilistic principal components analysis [29,30]. Data was

transformed to normality using the procedures of Box and Cox

[31]. Differences in means were determined using 2-tailed t-tests or

Mann-Whitney U-tests if normality was not achieved. Data

transformations, multivariate analyses and non-parametric statis-

tics were performed using the Excel Add-In imDEV [32], which

provides a graphical user interface to the R Statistical Computing

Environment. Hierarchical clusters were based on a Minkowski

distance matrix agglomerated using Ward’s minimum variance

method. PLS-DA was performed using the SIMPLS algorithm

with leave one out cross-validation, and predictive models were

iteratively compared (n = 100) to those built with data randomly

assigned to discriminate class data. Predictive models were

constructed to either classify animals based on relative dietary n-

3 content [discriminate classes: 0 = Control; 1 = DHA1;

10 = DHA2; 20 = DHA3] or atherosclerotic plaque area modeled

as a continuous variable. Model variable selection with correlation,

ANOVA, entropy minimization, and chi square filtering were

compared between 10 independent training/test set selections.

Correlation based filtering provided the model set with the lowest

variance in the root mean squared error of prediction.

Results

Effects of DHA Supplementation on Cardiovascular RiskParameters and Atherosclerosis

The dose-response effects of DHA supplementation were

evaluated on several cardiovascular risk factors (i.e. plasma and

hepatic lipids, sBP) and atherosclerosis as shown in Figure 1. The

doses of DHA given to the LDLR2/2 mice (0, 0.1, 1 and 2% of

energy as DHA) were negatively correlated with plasma TG and

TC (R2 = 0.97, p = 0.01 and R2 = 0.96, p,0.01 respectively) as

well as with liver contents of TG and TC (R2 = 0.89, p = 0.06 and

R2 = 0.93, p = 0.03) (Figure 1A). There was also a strong negative

correlation with atherosclerotic plaque area (R2 = 0.97, p = 0.02;

Figure 1B). With sBP, the correlation did not reach significance

(R2 = 0.84, p = 0.08; Figure 1B). The effects of DHA on

cardiovascular risk parameters and atherosclerosis were especially

strong in the DHA3 group (Table S4 in File S1) in which plasma



www.graphpad.com

levels of TG and TC were reduced (p,0.05) by 37 and 28%

respectively, while liver concentrations of TG and TC were

decreased (p,0.05) by 56 and 47% respectively. Concerning

atherosclerotic plaque extent (Figure 2) and sBP (Table S5 in File

S1), the reductions induced by DHA supplementation were also

especially pronounced in the DHA3 group (235 and 216%, p,

0.05 respectively).

Impact of DHA Supplementation on the Profiles of FattyAcids in Plasma and Liver

As expected, DHA supplementation produced a substantial

enrichment of n-3 PUFAs in both plasma and liver (Figure 3).

These changes were dose-dependent in both plasma (R2 = 0.95,

p,0.01) and liver (R2 = 0.99, p,0.01) (Figures S1 & S4 in File S1).

Similar changes were observed for plasma and liver levels of DHA

(R2 = 0.94, p,0.01 and R2 = 0.99, p,0.01 respectively, Table S6

and Figures S1 & S4 in File S1) which represents the major n-3

PUFAs in both biological compartments. EPA levels (Table S6

and Figures S1 & S4 in File S1) were also increased in a dose-

dependent manner (R2 = 0.96, p,0.01 and R2 = 0.99, p,0.01). In

plasma, n-3 PUFA increases were partially reflected in decreases in

n-6 PUFAs, while liver n-6 PUFA composition remained constant.

Finally, in both plasma and liver, the MUFA relative abundance

was significantly decreased, with the DHA3 group showing the

greatest change (216%, p,0.05 and 231%, p,0.05 respectively)

in comparison with the Control group.

Impact of DHA Supplementation on the Profiles ofPlasma Oxylipins and Liver Peroxidized Metabolites

Plasma and liver profiles of the main oxygenated metabolites

originating from the enzymatic and non-enzymatic oxidation of n-

6 and n-3 PUFAs are represented in Figure 4 and in Table S7 in

File S1. These graphs highlight that DHA supplementation not

only modified fatty acids profiles but also influenced the profiles of

oxygenated metabolites.

Regarding plasma oxylipins, all families (i.e. AA’s, EPA’s and

DHA’s oxylipins, Figure 4A) and sub-families (predominantly

represented by epoxides and secondarily by alcohols and diols,

Table S7 in File S1) were affected by DHA supplementation. It is

interesting to emphasize that the dose-responses differed by

oxylipin family leading to different levels of correlations between

the plasma content of the parent fatty acids (i.e. AA, EPA and

DHA) and their corresponding oxylipins (Figure S1 in File S1).

Indeed, the increase of EPA-derived oxylipins in plasma was

directly proportional to the dose of DHA and was positively

correlated with the plasma level of EPA (R2 = 0.99, p,0.01). This

latter effect was observed for each individual EPA-derived oxylipin

analyzed (i.e. 17(18)-EpETE, 14(15)-EpETE, 17,18-DiHETE,

14,15-DiHETE, 15-HEPE, 12-HEPE, 5-HEPE) (Figure S2 in

File S1). The correlation was much weaker between the plasma

level of DHA and the DHA-derived oxylipins (R2 = 0.74, ns)

(Figure S1 in File S1). However, when looking at the individual

DHA-derived oxylipins (i.e. 19(20)-EpDPE, 16(17)-EpDPE, 19,20-

DiHDPA,17-HDoHE respectively), it should be noticed that the

diol and alcohol species (i.e. 19,20-DiHDPA and 17-HDoHE)

were actually significantly correlated with plasma DHA concen-

tration (R2 = 0.99, p,0.01) (Figure S3 in File S1).

Concerning free radical-mediated peroxidation metabolites, the

liver levels of DHA-derived F4-NeuroPs were particularly affected

by DHA supplementation with concentrations being 2-fold (ns), 6-

fold (p,0.05) and 11-fold (p,0.05) increased in the DHA1,

DHA2 and DHA3 groups respectively (Figure 4B). This leads to

strong positive correlations between F4-NeuroPs and liver DHA

(Figure S4 in File S1, R2 = 0.99, p,0.01). However, the hepatic

concentrations of HHE-P, a hydroxyalkenal originating from all n-

3 PUFAs, were not significantly different between the groups and

correlation between levels of HHE-P and of n-3 PUFAs was not

significant (R2 = 0.76, ns). Finally, concentrations of HNE-P were

reduced in response to DHA supplementation and correlation

between liver levels of HNE-P and of n-6 PUFAs was not

significant (R2 = 0.52, ns). It should be noted that an additional

systemic oxidative stress parameter (i.e. oxygen radical absorbance

capacity or ORAC) was measured in plasma and no difference

appeared between groups (data not shown).

Predictive Variables of Plaque Area ReductionCorrelation analyses were used to assess the association of the

measured variables with changes in atherosclerotic plaque area.

Among the 131 variables assessed, the 30 most significantly

correlated with plaque area at p,0.001 are represented in

Figure 5. Positive correlations were seen with liver TG and TC,

the diet-induced change in sBP and the initial dBP as well as

plasma fatty acid and oxylipin levels including several n-6 PUFAs

and 5-hydroxyeicosatetraenoic acid (5-HETE), a putative pro-

inflammatory mediator derived from 5-lipoxygenase metabolism

of AA. Surprisingly, plasma TC and TG did not belong to the 30

most significantly correlated variables. Negative correlations with

plaque area first included the liver concentrations of the DHA-

derived F4-NeuroPs which was also the variable most negatively

correlated with plasma TC, a major cardiovascular risk factor

(data not shown). It should be noted that the hepatic concentration

of F4-NeuroPs was also negatively correlated with other cardio-

vascular risk factors (i.e. liver TC and TG, change in sBP).

Interestingly, none of the DHA-derived oxylipins in plasma

appeared in the list of the 9 variables most negatively correlated

with plaque area. In contrast, liver EPA and two plasma EPA-

derived oxylipins (15-HEPE, 14,15-DiHETE) belong to this list.

Finally, other variables negatively correlated with plaque area at

p,0.001 included the arachidonic acid derived 15-deoxyprosta-

glandin J2 (15-deoxyPGJ2) as well as the saturated fatty acid

stearate (C18:0).

To determine the covariant behavior of the measured variables,

a hierarchical cluster analysis and a PLS-DA (Figure 6) were

performed to identify variables which best discriminate plaque

area or dietary groups (Figure S5 in File S1). Variables were

separated into 7 unique variable clusters (Figure 6A). Clusters 1

and 2 were dominated by plasma and liver n-3 PUFAs and n-3

oxylipins, but also contained liver saturated fat, with cluster 2

containing 13 of 15 variables able to segregate the relative dose of

the diet groups (see * symbol in Figure 6A). In contrast, clusters 2,

4, 5 and 6 each contained variables responsible for plaque size

prediction (see # symbol in Figure 6A). It should be noted that the

liver F4-NeuroPs concentrations appear in both lists of discrim-

inant variables. The PLS discrimination of mice according to their

plaque area (Figure 6B) clearly shows the multi-dimensional

overlap of the Control and DHA-1 mice, as well as those from the

Control and DHA-2 groups, in accordance with the results shown

in Figure 2. Moreover, as shown in Figure 6C, groups were

discriminated using only 12 of the 152 variables collected,

representing 3 variable clusters. Of these, liver F4-NeuroPs and

the plasma 14,15-diol metabolite of EPA (14,15-DiHETE) were

the strongest negative predictors of plaque size, while plasma

MUFAs and liver TC were the best positive predictors of plaque

size in this LDLR2/2 model.



Figure 1. Correlations between the doses of DHA and the cardiovascular parameters (i.e. plasma TG and TC, liver TG and TC, sBP,and plaque area). LDLR2/2 mice were given by daily oral gavages (20 weeks) either oleic acid rich sunflower oil (Control group) or a mixture ofoleic acid rich sunflower oil and DHA rich tuna oil providing 0.1%, 1% or 2% of energy as DHA (DHA1, DHA2, and DHA3 groups respectively).doi:10.1371/journal.pone.0089393.g001



Discussion

The present study aimed to determine the impact of DHA

supplementation on the profiles of PUFA oxygenated metabolites

and to investigate their contribution to atherosclerosis prevention.

The major findings are as follows: 1) the profiles of both DHA and

EPA enzymatic and non-enzymatic metabolites were dose-

dependently modified in response to DHA supplementation; 2)

F4-NeuroPs issued from the free radical-mediated peroxidation of

DHA appeared to be a major source of oxygenated metabolites of

Figure 2. Quantification of atherosclerotic lesions. (A) Sections of aortic sinus (at 500 mm from the cusps) stained with oil red O. The arrowsindicate lipid deposit. (B) Atherosclerotic lesion severity is expressed as percent changes of lesion area in total cross-sectional area. Data representmeans 6 SEM (n = 20/group). a,b,c Mean values with unlike letters were significantly different (p,0.05).doi:10.1371/journal.pone.0089393.g002



DHA in the liver during atherogenesis, 3) Hepatic levels of F4-

NeuroPs were identified as major predictive variables of athero-

sclerosis prevention.

DHA Dose-dependently Decreases AtherosclerosisThe dose-response design of the present study not only

confirmed the atheroprotective effect of LC n-3 PUFAs previously

demonstrated in similar animal models [14,33,34] but also showed

a strong dose-dependent relationships between the dose of DHA

consumed, the atherosclerotic plaque extent, and lipemia. Indeed,

more than 90% of the reduction of these cardiovascular risk

parameters could be explained by the increased consumption of

DHA. Surprisingly, when looking at the Spearman’s correlation

heatmap (Figure 5) reporting the 30 most significantly correlated

variables with arteriosclerotic plaque, it should be noted that

plasma TG and TC do not belong to the list. This suggests that the

atheroprotective effects of DHA were in part independent of the

reduction of lipemia. This is consistent with results of several

animal studies showing that LC n-3 PUFAs can interfere at the

vascular level to prevent plaque inception, progression and

instability notably by reducing inflammatory processes [34,35].

Despite the dose-dependent relationships established between the

intake of DHA and the reduction of atherosclerotic plaque, the

nutritional dose (DHA1 group) showed no effect. This finding is

consistent with those of Wang et al. [36] which is, to the best of our

knowledge, the only other study investigating atheroprotective

effects of a nutritional dose of LC n-3 PUFAs in a murine model of

atherosclerosis. However, these findings in mice are inconsistent

with several human clinical trials showing that modest consump-

tion of fish (i.e., 1–2 servings/wk) reduces significantly coronary

death rate and progression of coronary artery atherosclerosis

[4,37].

DHA Supplementation Strongly Modifies PUFAsOxygenated Metabolite Profiles

DHA supplementation dose-dependently increased the propor-

tion of LC n-3 PUFAs at the expense of MUFAs and n-6 PUFAs

in plasma while in liver only MUFA proportions were reduced.

The increases of LC n-3 PUFAs levels were logically allocated to

DHA but also to EPA suggesting that retroconversion occurred

since no EPA was provided by the diet. Retroconversion of DHA

into EPA is a well-described metabolic process which was initially

Figure 3. Plasma (A) and liver (B) fatty acid profiles (% total FA). The 4 families of fatty acid (i.e. saturated fatty acids (SFA), monounsaturatedfatty acids (MUFA), n-6 PUFA and n-3 PUFA) are represented with different grey shades from black to light grey. Data represent means 6 SEM (n = 10/group).doi:10.1371/journal.pone.0089393.g003

Figure 4. Profiles of PUFAs’s oxygenated metabolites. (A) Plasma concentrations of oxylipins originating from AA (20:4 n-6), EPA (20:5 n-3),and DHA (22:6 n-3). (B) Liver contents of peroxidation metabolites originating from n-6 PUFAs (4-HNE-P), n-3 PUFAs (4-HHE-P) and DHA (F4-NeuroPs).Data represent means 6 SEM (n = 10/group). a,b,c Mean values with unlike letters were significantly different (p,0.05).doi:10.1371/journal.pone.0089393.g004



studied in rat hepatocytes and has been reported in several

experimental models as well as in humans [38]. Overall, the

modifications of plasma and liver fatty acid profiles reported here

are consistent with findings in both hypercholesterolemic rabbits

[23] and Sprague-Dawley rats [39] given similar dietary levels of

DHA. Our experimental design did not allow determining the

fatty acid profiles of aorta. However, increased levels of DHA and

EPA in aorta [40], heart [39] or macrophages [36] have been

reported in rodents given n-3 PUFA doses similar to the present

study. Therefore, n-3 PUFA levels in the aorta have probably been

enhanced in our model too and the atheroprotective effects

obtained are probably in part due to the local action of n-3

PUFAs.

Beside their incorporation into cellular lipids, n-3 PUFAs are

susceptible to oxidation by enzymatic or non-enzymatic reactions

leading to a large array of metabolites. Enzymatic pathways

involve cyclooxygenase and lipooxygenases but also cytochrome

P450 epoxygenase and epoxide hydrolase which produce various

alcohols, epoxides and diols [7] referred to here as oxylipins. Some

of these molecules, namely the alcohols and diols, and additional

oxygenated metabolites can arise from the non-enzymatic

oxidation of PUFAs by reactive oxygen species and notably

Figure 5. A Spearman’s correlation heatmap. Variables rank from the most positive to most negative correlation with arteriosclerotic plaquearea. The displayed variables are the 30 most significantly correlated with arteriosclerotic plaque area at p,0.001 with orange indicating positive andblue indicating negative correlations.doi:10.1371/journal.pone.0089393.g005



Figure 6. Hierarchical cluster and Partial Least Squares Discrimination Analysis (PLS-DA). (A) Complete data sets (n = 10/group) weresegregated into 7 unique clusters of variables (C1–C7) by hierarchical cluster analysis. Clusters were assigned unique colors and used to highlightvariables in PLS-DA. Plasma and liver variables are indicated by yellow triangles and orange inverted triangles, respectively. Dominant variables forfeeding group discrimination are identified by asterisks (*). Dominant variables for plaque area discrimination are identified by open circles (#). (B)Animals eating each dietary mixture with complete data sets (n = 10/group) were partially segregated by PLS-DA. Mice from DHA1 group wereindistinguishable from Controls (p,0.05), while DHA2 (p,0.0001) and DHA3 (p,1.5E-07) were significantly different in this model. (C) Plasma (yellowtriangle) and liver (orange triangle) metabolites belonging to clusters C2, C4, and C6 as identified in (A) were included in this model. Predictivevariables are labeled with their point size, indicating relative selection frequency of 20–80% in 10 models constructed using a Pearson’s-correlationvariable selection filter which out performed other filters in terms of the minimum root mean squared error of prediction (RMSEP = 3.461). Analytesappearing in $30% of models were retained for the final predictive model construction.doi:10.1371/journal.pone.0089393.g006



include hydroxyalkenals (HNE and HHE from n-6 and n-3

PUFAs respectively) and F2-IsoPs/F4-NeuroPs (from AA and

DHA respectively) [9,10,11]. Little is known about the impact of

n-3 PUFA supplementation on the profiles of their specific

oxygenated metabolites. Moreover, to the best of our knowledge,

no study has reported so far the profile of enzymatic and non-

enzymatic oxygenated metabolites of n-3 PUFAs in a model of

atherosclerosis. Though, this could be particularly relevant since

both the enzymatic and non-enzymatic pathways are known to be

enhanced during atherogenesis [12,41] and high oxylipins levels

have been reported in ApoE-/2 mice [42]. Concerning the

enzymatic metabolites, DHA supplementation induced substantial

modifications of their plasma profiles with a reduction of the AA

oxylipins and increases of EPA and DHA oxylipins. Interestingly,

the impact on EPA oxylipins was higher than on DHA oxylipins

probably because of the higher change in their precursor fatty

acids. In healthy humans given EPA+DHA supplement (,1.4%

daily energy intake, i.e. close to DHA2 group) for 4 weeks [7],

similar modifications of the oxylipins profiles were reported, but to

a lesser extent than ours, especially for the effects on AA and EPA

oxylipins. Inter-individual variability within the study cohort likely

reduced the average efficacy reported in this human intervention

study [43]. Concerning the dose-response relationships, it should

be emphasized that the present data reveal different levels of

correlation between EPA and DHA with their corresponding

oxylipins. Indeed, the plasma levels of EPA oxylipins were strongly

and positively correlated with plasma EPA concentration as

previously shown in human [44]. This effect that was less

pronounced for DHA and its corresponding oxylipins when

considering the entire family of oxylipins. However, when looking

at the individual DHA-derived oxylipins, correlations were much

stronger with diol and alcohol species (i.e. 19,20-DiHDPA and 17-

HDoHE) which are actually produced both by enzymatic and

non-enzymatic pathways. Plasma DHA also showed strong

positive correlations with liver F4-NeuroP levels suggesting that

DHA was readily peroxidized. This is consistent with previous

findings in LDLR2/2 mice [33] exposed to LC n-3 PUFAs (34 mg

of EPA +23 mg of DHA/day/mice, i.e. ,3% daily energy intake)

for 12 weeks, which showed 5-fold and 3-fold increased F4-

NeuroPs levels in the liver and heart respectively. This suggests

that a proportion of DHA incorporated in tissue lipids is readily

peroxidized and F4-NeuroPs is likely to be a relevant biomarker of

this non-enzymatic oxidation pathway. In this study, we reported a

maximal liver F4-NeuroP increase of 11-fold with the highest dose

(35.5 mg/day/mice) DHA3 group vs. Control. These results

together with other studies [33] thus demonstrate that DHA

supplementation of atherosclerotic mice induces a dose-dependent

increase of F4-NeuroPs in peripheral tissues, and/or cell types that

accumulate DHA such as aorta [40] and immune cells [36].

F4-NeuroPs as a Major Predictive Variable ofAtherosclerosis Prevention

The analyses of the relationships between plaque extent and the

other variables measured using correlation analyses, hierarchical

cluster and PLS-DA indicated that among the ,131 variables

measured, the liver content of DHA-derived F4-NeuroPs was the

most negatively correlated variable with plaque extent. Moreover,

when a correlation strength filter was used for variable selection in

predictive model production, the F4-NeuroPs was one of just two

predictive variables needed to predict atherosclerosis prevention.

This strongly reinforces the initial hypothesis suggesting that

peroxidized metabolites of DHA could contribute to its ather-

oprotective effects and suggests that F4-NeuroPs could be relevant

candidates. However, it must be emphasized that these are

correlative associations, and causal linkage remains to be further

assessed. Moreover, it cannot be excluded that other lipid

mediators could have contributed to the reduction of atheroscle-

rosis. For instance, human atherosclerotic lesions have been shown

to over express 12- and 15-lipoxygenases [45] and DHA-

dependent impacts on cardiovascular disease are influenced by

the activity of these enzymes [46]. It should also be emphasized

that despite an absence of EPA intake, the current results revealed

that the levels of EPA and its corresponding oxylipins were

substantially increased by DHA supplementation. Moreover, the

level of 14,15-DiHETE, a stable metabolite of the EPA and a

cytochrome P450 product, showed a negative correlation with

plaque size. All together, these data suggest that F4-NeuroPs are

putative contributors of atherosclerosis prevention in coordination

with other lipid mediators.

The present study is to the best of our knowledge, the first one to

establish in vivo an inverse dose-dependent relationship between

the productions of DHA-derived peroxidized metabolites and

atherosclerosis development. Nevertheless, our results are consis-

tent with several lines of evidence demonstrated in vitro. Indeed,

the group of Sethi et al. [47] demonstrated that oxidized EPA and

DHA (obtained by ex-vivo oxidation of the native fatty acids with

CuSO4 and ascorbic acid) were able to reduce adhesion of U937

monocyte cells to endothelial cells and decrease the expression of

adhesion molecules whereas native EPA and DHA had no effect.

The bioactive oxygenated metabolites issued from EPA and DHA

were not identified but the authors clearly showed that the non-

enzymatic oxidation of EPA and DHA was a mandatory

prerequisite to make them bioactive on endothelial cells. The

reduction of adhesion molecules expression was achieved through

the inhibition of NFkB binding activity and was latter associated

with a PPARc dependent mechanism [48]. Equally, it has been

nicely shown that peroxidation of DHA was responsible for its

effects on transient outward current and steady-state outward

current in rat ventricular myocytes [49]. The group of Morrow

and Roberts, pioneers in the in vivo identification of IsoP/NeuroPs

[10,50] substantially contributed to this field of research by

demonstrating that cyclopentenone A4/J4-NeuroPs issued from

the peroxidation of DHA were anti-inflammatory mediators in the

RAW267.4 murine macrophage cell line [51]. Similar results were

reported recently with 15-A3t-IsoPs [52], a specific peroxidized

metabolite of EPA. These two studies confirmed the inhibition of

the NFkB pathway as a major mechanism of action of EPA and

DHA peroxidized metabolites. Finally, even though the in vivo

study published by Saraswathi et al. [33] did not investigate the

relationship between the production of F4-NeuroPs and athero-

sclerosis regression, the authors did speculate that the F4-NeuroPs

could play a role in the prevention of atherosclerosis. All together,

these results challenge the long standing paradigm suggesting that

peroxidized metabolites of PUFAs might only be cytotoxic

molecules.

In conclusion, the present study showed that supplementation

with DHA during atherogenesis is associated with the production

of an array of oxygenated metabolites in association with reduced

atherosclerosis progression. Among them, the F4-NeuroPs arising

from the peroxidation of DHA were found to be a new potentially

relevant biomarker of DHA exposure and one of the best

predictive variables of atherosclerosis prevention in the LDLR2/

2 mouse. Further investigations are needed to determine if F4-

Neuroprostanes can contribute to the anti-atherogenic effects of

DHA and decipher their molecular mechanisms of action. This

will help to elucidate novel interactions between lipid peroxidation

metabolites and atherosclerosis.



Supporting Information

File S1 Figure S1, Correlations between doses of DHAgiven to LDLR-/- mice and plasma levels of PUFA and ofAA, EPA, and DHA and their corresponding oxylipins.Figure S2, Correlations between plasma levels of EPA,and their corresponding specific oxylipins. Figure S3,Correlations between plasma levels of DHA, and theircorresponding specific oxylipins. Figure S4, Correla-tions between doses of DHA given to LDLR-/- mice andliver levels of PUFA and plasma levels of n-6 PUFA, n-3PUFA and DHA and their corresponding peroxidizedmetabolites. Figure S5, Partial least squares discrimi-nation analysis of dietary groups in LDLR-/- given bydaily oral gavages increasing doses of DHA. (A) Animals

eating each dietary mixture with complete data sets (n = 10/group)

were segregated by PLS analyses. (B) Group segregation was

driven by plasma (yellow triangle) and liver (orange triangle)

metabolites belonging to clusters C2, C3, and C4 as identified in

Figure 7. The plasma EPA metabolite, 17,18-dihydroxyeicosaote-

traenoic acid (17,18-DiHETE) and the liver DHA metabolite

group F4-neuroprostanes (F4-NeuroPs) were the most frequently

selected variables occurring in 80% and 60% of models,

respectively. Figure S6, Correlation between plasma F2-isoprostanes and 9-HETE levels. Figure S7, Correlationsbetween plasma oxylipins and plasma 9-HETE levels.

Table S1, Surrogates recoveries. Table S2, Oxylipinassay UPLC solvent gradient. Table S3, UPLC/MS-MSparameters of metabolites measured in plasma. TableS4, Concentrations of plasma* and liver{ triglycerides(TG) and total cholesterol (TC). Table S5, Changes insystolic and diastolic blood pressures (sBP and dBP),and heart rate (HR). Table S6, Plasma and liver levels ofmajor polyunsaturated fatty acids (PUFA). LDLR-/- mice

were given by daily oral gavages (20 weeks) either oleic acid rich

sunflower oil (Control group) or a mixture of oleic acid rich

sunflower oil and DHA rich tuna oil providing 0.1%, 1% or 2% of

energy as DHA (DHA1, DHA2, and DHA3 groups respectively).

Table S7, Plasma levels of PUFAs-derived oxylipins.LDLR-/- mice were given by daily oral gavages (20 weeks) either

oleic acid rich sunflower oil (Control group) or a mixture of oleic

acid rich sunflower oil and DHA rich tuna oil providing 0.1%, 1%

or 2% of energy as DHA (DHA1, DHA2, and DHA3 groups

respectively). Table S8, Thromboxane and prostaglandinstability through sample processing. Table S9, Fatty acidtriol stability through sample processing. Table S10,Fatty acid diol stability through sample processing.

(DOCX)

Acknowledgments

All authors were involved in the project and participated either to the

design of the study, the analysis of the biological samples or the

interpretation of the results. The authors acknowledge Christophe

Del’Homme, Anne Terisse-Lottier, Alexandre Teynie and Jean-Paul

Rigaudiere for their excellent assistance during the animal experiment.

We also thank Catherine Besson, Dominique Bayle and Severine Thien for

their important contribution during samples collection and the analyses of

biochemical biomarkers. We are also grateful to Charlotte Joly and

Georges Da Silva for their involvement in the analyses of lipid peroxidation

biomarkers and their dedication in the method developments. Finally,

thanks to Brigitte Laillet and Jean-Paul Rigaudiere for the fatty acid

analyses of liver samples and William Keyes for technical support in plasma

fatty acid analyses.

Author Contributions

Conceived and designed the experiments: CG BC AM. Performed the

experiments: CG JWN TD TLP JMG CD OB EPG. Analyzed the data:

CG JWN TD TLP AM BC. Contributed reagents/materials/analysis

tools: JWN TLP TD JMG CD OB EPG. Wrote the paper: CG JWN TD

AM BC.

References

1. Miles EA, Calder PC (2012) Influence of marine n-3 polyunsaturated fatty acids

on immune function and a systematic review of their effects on clinical outcomes

in rheumatoid arthritis. Br J Nutr 107 Suppl 2: S171–184.

2. Calder PC (2012) The role of marine omega-3 (n-3) fatty acids in inflammatory

processes, atherosclerosis and plaque stability. Mol Nutr Food Res 56: 1073–

1080.

3. Brown AL, Zhu X, Rong S, Shewale S, Seo J, et al. (2012) Omega-3 fatty acids

ameliorate atherosclerosis by favorably altering monocyte subsets and limiting

monocyte recruitment to aortic lesions. Arterioscler Thromb Vasc Biol 32:

2122–2130.

4. Erkkila AT, Lichtenstein AH, Mozaffarian D, Herrington DM (2004) Fish intake

is associated with a reduced progression of coronary artery atherosclerosis in

postmenopausal women with coronary artery disease. Am J Clin Nutr 80: 626–

632.

5. De Caterina R, Zampolli A (2001) n-3 fatty acids: antiatherosclerotic effects.

Lipids 36 Suppl: S69–78.

6. Serhan CN, Krishnamoorthy S, Recchiuti A, Chiang N (2011) Novel anti-

inflammatory–pro-resolving mediators and their receptors. Curr Top Med

Chem 11: 629–647.

7. Shearer GC, Harris WS, Pedersen TL, Newman JW (2010) Detection of omega-

3 oxylipins in human plasma and response to treatment with omega-3 acid ethyl

esters. J Lipid Res 51: 2074–2081.

8. Cosgrove JP, Church DF, Pryor WA (1987) The kinetics of the autoxidation of

polyunsaturated fatty acids. Lipids 22: 299–304.

9. Jahn U, Galano J, Durand T (2008) Beyond prostaglandins - Chemistry and

biology of cyclic oxygenated metabolites fromed by free-radical pathways from

polyunsaturated fatty acids. Angew Chem Int Ed 47: 5894–5955.

10. Roberts LJ 2nd, Montine TJ, Markesbery WR, Tapper AR, Hardy P, et al.

(1998) Formation of isoprostane-like compounds (neuroprostanes) in vivo from

docosahexaenoic acid. J Biol Chem 273: 13605–13612.

11. Milne GL, Yin H, Hardy KD, Davies SS, Roberts LJ 2nd (2011) Isoprostane

generation and function. Chem Rev 111: 5973–5996.

12. Stocker R, Keaney JF Jr (2005) New insights on oxidative stress in the artery

wall. J Thromb Haemost 3: 1825–1834.

13. Simopoulos AP, Leaf A, Salem N Jr (2000) Workshop statement on the

essentiality of and recommended dietary intakes for Omega-6 and Omega-3

fatty acids. Prostaglandins Leukot Essent Fatty Acids 63: 119–121.

14. Zampolli A, Bysted A, Leth T, Mortensen A, De Caterina R, et al. (2006)Contrasting effect of fish oil supplementation on the development of

atherosclerosis in murine models. Atherosclerosis 184: 78–85.

15. Nicoletti A, Kaveri S, Caligiuri G, Bariety J, Hansson GK (1998) Immuno-

globulin treatment reduces atherosclerosis in apo E knockout mice. J Clin Invest102: 910–918.

16. Mazur A, Remesy C, Gueux E, Levrat MA, Demigne C (1990) Effects of diets

rich in fermentable carbohydrates on plasma lipoprotein levels and on

lipoprotein catabolism in rats. J Nutr 120: 1037–1045.

17. Smedes F (1999) Determination of total lipid using non-chlorinated solvents.Analyst 124: 1711–1718.

18. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, et al. (2011) The human

serum metabolome. PLoS One 6: e16957.

19. Newman JW, Watanabe T, Hammock BD (2002) The simultaneous

quantification of cytochrome P450 dependent linoleate and arachidonatemetabolites in urine by HPLC-MS/MS. J Lipid Res 43: 1563–1578.

20. Newman JW, Kaysen GA, Hammock BD, Shearer GC (2007) Proteinuriaincreases oxylipid concentrations in VLDL and HDL but not LDL particles in

the rat. J Lipid Res 48: 1792–1800.

21. Shearer GC, Newman JW (2008) Lipoprotein lipase releases esterified oxylipinsfrom very low-density lipoproteins. Prostaglandins Leukot Essent Fatty Acids 79:

215–222.

22. Perera SK, Fedor LR (1979) Acid-catalyzed and base-catalyzed dehydration of

prostaglandin-e2 to prostaglandin-a2 and general-base-catalyzed isomerizationof prostaglandin-a2 to prostaglandin-b2. Journal of the American Chemical

Society 101: 7390–7393.

23. Gladine C, Roy NC, Rigaudiere JP, Laillet B, Da Silva G, et al. (2011)

Increasing intake of long-chain n-3 PUFA enhances lipoperoxidation andmodulates hepatic gene expression in a dose-dependent manner. Br J Nutr: 1–

20.

24. Musiek ES, Cha JK, Yin H, Zackert WE, Terry ES, et al. (2004) Quantification

of F-ring isoprostane-like compounds (F4-neuroprostanes) derived from



docosahexaenoic acid in vivo in humans by a stable isotope dilution mass

spectrometric assay. J Chromatogr B Analyt Technol Biomed Life Sci 799: 95–102.

25. Zhao Z, Hjelm NM, Lam CW, Ho CS (2001) One-step solid-phase extraction

procedure for F(2)-isoprostanes. Clin Chem 47: 1306–1308.26. Oger C, Brinkmann Y, Bouazzaoui S, Durand T, Galano JM (2008)

Stereocontrolled access to isoprostanes via a bicyclo[3.3.0]octene framework.Org Lett 10: 5087–5090.

27. Oger C, Bultel-Ponce V, Guy A, Balas L, Rossi JC, et al. (2010) The handy use

of Brown’s P2-Ni catalyst for a skipped diyne deuteration: application to thesynthesis of a [D4]-labeled F4t-neuroprostane. Chemistry 16: 13976–13980.

28. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ(2006) Centering, scaling, and transformations: improving the biological

information content of metabolomics data. BMC Genomics 7: 142.29. Qu L, Li L, Zhang Y, Hu J (2009) PPCA-Based Missing Data Imputation for

Traffic Flow Volume: A Systematical Approach. IEEE Transactions on

intelligent transportation systems 10: 512–521.30. Wang C, Wang W (2006) Links between PPCA and subspace methods for

complete Gaussian density estimation. IEEE Trans Neural Netw 17: 789–792.31. Box GEP, Cox DR (1964) An analysis of transformations. Journal of the Royal

Statisistical Society 26: 211–252.

32. Grapov D, Newman JW (2012) imDEV: a Graphical User Interface to RMultivariate Analysis Tools in Microsoft Excel. Bioinformatics.

33. Saraswathi V, Gao L, Morrow JD, Chait A, Niswender KD, et al. (2007) Fish oilincreases cholesterol storage in white adipose tissue with concomitant decreases

in inflammation, hepatic steatosis, and atherosclerosis in mice. J Nutr 137: 1776–1782.

34. Matsumoto M, Sata M, Fukuda D, Tanaka K, Soma M, et al. (2008) Orally

administered eicosapentaenoic acid reduces and stabilizes atherosclerotic lesionsin ApoE-deficient mice. Atherosclerosis 197: 524–533.

35. Nakajima K, Yamashita T, Kita T, Takeda M, Sasaki N, et al. (2011) Orallyadministered eicosapentaenoic acid induces rapid regression of atherosclerosis

via modulating the phenotype of dendritic cells in LDL receptor-deficient mice.

Arterioscler Thromb Vasc Biol 31: 1963–1972.36. Wang S, Wu D, Matthan NR, Lamon-Fava S, Lecker JL, et al. (2009) Reduction

in dietary omega-6 polyunsaturated fatty acids: eicosapentaenoic acid plusdocosahexaenoic acid ratio minimizes atherosclerotic lesion formation and

inflammatory response in the LDL receptor null mouse. Atherosclerosis 204:147–155.

37. Mozaffarian D, Rimm EB (2006) Fish intake, contaminants, and human health:

evaluating the risks and the benefits. JAMA 296: 1885–1899.38. Brossard N, Croset M, Pachiaudi C, Riou JP, Tayot JL, et al. (1996)

Retroconversion and metabolism of [13C]22: 6n-3 in humans and rats afterintake of a single dose of [13C]22: 6n-3-triacylglycerols. Am J Clin Nutr 64:

577–586.

39. Saito M, Ueno M, Kubo K, Yamaguchi M (1998) Dose-Response Effect of

Dietary Docosahexaenoic Acid on Fatty Acid Profiles of Serum and Tissue

Lipids in Rats. J Agric Food Chem 46: 184–193.

40. Adan Y, Shibata K, Sato M, Ikeda I, Imaizumi K (1999) Effects of

docosahexaenoic and eicosapentaenoic acid on lipid metabolism, eicosanoid

production, platelet aggregation and atherosclerosis in hypercholesterolemic

rats. Biosci Biotechnol Biochem 63: 111–119.

41. Vila L (2004) Cyclooxygenase and 5-lipoxygenase pathways in the vessel wall:

role in atherosclerosis. Med Res Rev 24: 399–424.

42. Ulu A, Davis BB, Tsai HJ, Kim IH, Morisseau C, et al. (2008) Soluble epoxide

hydrolase inhibitors reduce the development of atherosclerosis in apolipoprotein

e-knockout mouse model. J Cardiovasc Pharmacol 52: 314–323.

43. Keenan AH, Pedersen TL, Fillaus K, Larson MK, Shearer GC, et al. (2012)

Basal omega-3 fatty acid status affects fatty acid and oxylipin responses to high-

dose n3-HUFA in healthy volunteers. J Lipid Res 53: 1662–1669.

44. Schuchardt JP, Schmidt S, Kressel G, Dong H, Willenberg I, et al. (2013)

Comparison of free serum oxylipin concentrations in hyper- vs. normolipidemic

men. Prostaglandins Leukot Essent Fatty Acids 89: 19–29.

45. Gertow K, Nobili E, Folkersen L, Newman JW, Pedersen TL, et al. (2011) 12-

and 15-lipoxygenases in human carotid atherosclerotic lesions: associations with

cerebrovascular symptoms. Atherosclerosis 215: 411–416.

46. Stephensen CB, Armstrong P, Newman JW, Pedersen TL, Legault J, et al.

(2011) ALOX5 gene variants affect eicosanoid production and response to fish

oil supplementation. J Lipid Res 52: 991–1003.

47. Sethi S, Eastman AY, Eaton JW (1996) Inhibition of phagocyte-endothelium

interactions by oxidized fatty acids: a natural anti-inflammatory mechanism?

J Lab Clin Med 128: 27–38.

48. Sethi S, Ziouzenkova O, Ni H, Wagner DD, Plutzky J, et al. (2002) Oxidized

omega-3 fatty acids in fish oil inhibit leukocyte-endothelial interactions through

activation of PPAR alpha. Blood 100: 1340–1346.

49. Jude S, Bedut S, Roger S, Pinault M, Champeroux P, et al. (2003) Peroxidation

of docosahexaenoic acid is responsible for its effects on I TO and I SS in rat

ventricular myocytes. Br J Pharmacol 139: 816–822.

50. Brooks JD, Milne GL, Yin H, Sanchez SC, Porter NA, et al. (2008) Formation of

highly reactive cyclopentenone isoprostane compounds (A3/J3-isoprostanes)

in vivo from eicosapentaenoic acid. J Biol Chem 283: 12043–12055.

51. Musiek ES, Brooks JD, Joo M, Brunoldi E, Porta A, et al. (2008) Electrophilic

cyclopentenone neuroprostanes are anti-inflammatory mediators formed from

the peroxidation of the omega-3 polyunsaturated fatty acid docosahexaenoic

acid. J Biol Chem 283: 19927–19935.

52. Brooks JD, Musiek ES, Koestner TR, Stankowski JN, Howard JR, et al. (2011)

The fatty acid oxidation product 15-A3t-isoprostane is a potent inhibitor of

NFkappaB transcription and macrophage transformation. J Neurochem 119:

604–616.



1

Table S1. Surrogates recoveries

Compound Recoveries

(%) Methylation Efficiency

15:1n5 80.4 ± 12%

Extraction and GC-MS Analysis Surrogates

d31-16:0-TG 75.1 ± 17% d35-18:0-PC 75.1 ± 15% 22:1n9-CE 75.5 ± 17%

22:3n3 75.5 ± 13%

Hydrolysis Control, SPE Isolation & LC-MS/MS Analysis Surrogates

d4-6-keto-PGF1a 62.1 ± 6.2 d4-TXB2 ND d4-PGF2a 55.1 ± 6.9 d4-PGD2 ND d3-LTE4 ND d4-LTB4 87.5 ± 13

d11-14,15-DiHETrE 91.2 ± 12

d6-20-HETE 48.7 ± 15 d4-9-HODE 75.1 ± 13 d8-12-HETE 59.1 ± 8.9 d8-5-HETE 90.8 ± 24

d4-12(13)-EpOME 35.4 ± 10

1

Table S2. Oxylipin assay UPLC solvent gradient

Time (min) Solvent A (%)

0 75 1 60

2.5 58 4.5 50 10.5 35 12.5 25 14 15

14.5 5 15 75 16 75

Solvent A = 0.1% acetic acid

Solvent B = 90:10 v/v acetonitrile/isopropanaol

1

Table S3. UPLC/MS-MS parameters of metabolites measured in plasma

Compound tR (min)

Acquisition Period

Precursor (m/z)

Product (m/z)

Dwell Time (msec)

DCP (V)

CE (V)

IS/ Surrogate IDLa (nM)

PHAU 3.34 1 249.2 130.1 10 -45 -20 IS

20-carboxy-LTB4 3.58 1 365.3 347.2 10 -70 -25 d4 6-keto PGF1a 3.54 Resolvin E1 3.71 1 349.3 195.2 50 -70 -25 d4 6-keto PGF1a 0.195

6-keto-PGF1a 3.71 1 369.3 163.1 10 -70 -40 d4 6-keto PGF1a 0.692 d4 6-keto PGF1a 3.71 1 373.3 167.1 10 -60 -40 PHAU IS

20-hydroxy-LTB4 3.8 1 351.3 195.2 10 -65 -25 d4 6-keto PGF1a 0.58 PGE3 4.53 2 349.3 269.2 10 -45 -22 d4-PGD2a ---

d4-TXB2 4.52 2 373.3 173.1 10 -50 -25 PHAU IS, --- TXB2 4.54 2 369.3 169.1 10 -50 -25 d4-TXB2 ---

9,12,13-TriHOME 5 2 329.2 211.2 10 -70 -32 d4-PGF2a 0.704 d4-PGF2a 5.06 2 357.3 197.2 10 -65 -35 PHAU IS

PGF2a / F2-IsoPs 5.07 2 353.3 193.2 10 -65 -35 d4-PGF2a 0.749 9,10,13-TriHOME 5.11 2 329.2 171.1 10 -70 -30 d4-PGF2a 0.903

PGE2 5.28 2 351.3 271.2 10 -35 -25 d4-PGD2a --- PGE1 5.36 2 353.3 317.2 10 -50 -22 d4-PGD2a ---

d4-PGD2 5.49 2 355.3 275.2 10 -45 -26 PHAU IS, --- PGD2 5.62 2 351.3 271.2 10 -35 -25 d4-PGD2a ---

Resolvin D1 6.12 3 375.3 121.1 50 -50 -40 d4-PGF2a 0.301 11,12,15 THET 6.03 3 353.3 167.1 50 -60 -30 d4-PGF2a 0.0848

Lipoxin A4 6.22 3 351.3 217.2 50 -50 -26 d4-PGF2a 0.335 a IDL = Lowest detectable level in plasma nM equivalents; Alkali unstable (---); uncalibrated data collection (UnCal); Internal Standards (IS)

2

Table S3. UPLC/MS-MS parameters of metabolites measured in plasma (continued)

Compound tR (min)

Acquisition Period

Precursor (m/z)

Product (m/z)

Dwell Time (msec)

DCP (V)

CE (V)


PGJ2/ δ 12-PGJ2 7.13 4 333.2 233.2 5 -52 -14 d4-PGF2a --- PGB2 7.11 4 333.3 235.2/175.1 5 -74 -28 d4-PGF2a --- LTB5 7.35 4 333.3 195.2 5 -70 -20 d4-PGF2a 0.505

DiHETHF 7.41 4 353.3 127.1 30 -55 -35 d11-14,15-DiHETrE --- 15,16-DiHODE 7.69 5 311.2 235.2 10 -65 -22 d11-14,15-DiHETrE 1.68 12,13-DiHODE 7.79 5 311.2 183.2 10 -70 -30 d11-14,15-DiHETrE 1.69 8,15-DiHETE 7.8 5 335.3 235.2 10 -65 -22 d11-14,15-DiHETrE 0.994 Hepoxilin A3 7.81 5 335.2 171.1 50 -85 -20 d11-14,15-DiHETrE --- 9,10-DiHODE 7.83 5 311.2 201.2 10 -65 -30 d11-14,15-DiHETrE 1.42

d3-LTE4 7.88 5 441.4 336.3 50 -80 -30 CUDA IS, --- LTE4 7.91 5 438.4 333.3 50 -60 -25 d3-LTE4 ---

17,18-DiHETE 8.08 5 335.3 247.2 10 -60 -25 d11-14,15-DiHETrE 0.377 5,15-DiHETE 8.15 5 335.3 173.1 10 -45 -21 d11-14,15-DiHETrE 0.304 6-trans-LTB4 8.26 5 335.3 195.2 20 -70 -21 d4-LTB4 0.601

14,15-DiHETE 8.42 5 335.3 207.2 10 -55 -25 d11-14,15-DiHETrE 0.322 CUDA 8.47 5 339.4 214.2 10 -65 -35 IS

d4-LTB4 8.49 5 339.3 163.1 20 -70 -38 CUDA IS LTB4 8.6 5 335.3 195.2 10 -70 -21 d4-LTB4 0.654

12,13-DiHOME 8.81 6 313.3 183.2 5 -70 -30 d11-14,15-DiHETrE 1.69 10,11-DHHep 8.9 6 301.2 283.2 10 -70 -30 CUDA IS 9,10-DiHOME 9.23 6 313.3 201.2 5 -70 -30 d11-14,15-DiHETrE 1.01

d11-14,15-DiHETrE 9.39 6 348.4 207.2 10 -64 -28 CUDA IS LTD4 9.41 6 495.45 177.1 50 -70 -25 d3-LTE4 ---

a IDL = Lowest detectable level in plasma nM equivalents; Alkali unstable (---); uncalibrated data collection (UnCal); Internal Standards (IS)

3


Compound tR (min)

Acquisition Period

Precursor (m/z)

Product (m/z)

Dwell Time (msec)

DCP (V)

CE (V)


19,20-DiHDoPE 9.44 6 361.3 273.2 10 -74 -24 d11-14,15-DiHETrE 1.22 14,15-DiHETrE 9.48 6 337.3 207.2 10 -65 -25 d11-14,15-DiHETrE 0.737 11,12-DiHETrE 10.06 7 337.3 167.1 10 -60 -27 d11-14,15-DiHETrE 0.753

9-HOTE 10.32 7 293.2 171.1 10 -65 -22 d4-9-HODE 0.245 12(13)-Ep-9-KODE 10.33 7 309.2 291.2 10 -65 -20 d4-9-HODE 0.261

13-HOTE 10.44 7 293.2 195.2 10 -65 -25 d4-9-HODE 0.416 8,9-DiHETrE 10.61 7 337.3 127.1 10 -55 -30 d11-14,15-DiHETrE 0.659

15-deoxy PGJ2 10.68 7 315.2 271.2 10 -60 -20 d11-14,15-DiHETrE 0.211 d6-20-HETE 10.86 7 325.3 281.2 10 -70 -25 CUDA IS

15-HEPE 10.88 7 317.2 219.2 10 -60 -20 d8-12-HETE 1.79 20-HETE 10.91 7 319.2 275.2 10 -65 -24 d6-20-HETE 1.22 12-HEPE 11.23 7 317.2 179.1 10 -60 -20 d8-12-HETE 1.83

5,6-DiHETrE 11.38 7 337.3 145.1 10 -70 -25 d11-14,15-DiHETrE 0.633 13-HODE 11.84 8 295.2 195.2 30 -65 -25 d4-9-HODE 3.43 5-HEPE 11.84 8 317.2 115.1 10 -60 -20 d8-5-HETE 1.19

d4-9-HODE 11.89 8 299.2 172.1 10 -70 -26 CUDA IS 9-HODE 11.95 8 295.2 171.1 10 -60 -25 d4-9-HODE 2.17

15(16)-EpODE 12.02 8 293.2 275.2 50 -60 -20 d4-12(13)-EpOME 0.505 17(18)-EpETE 12.11 8 317.2 259.2 30 -55 -15 d4-12(13)-EpOME 0.688

15-HETE 12.13 8 319.2 219.2 50 -70 -16 d8-12-HETE 0.313 13-KODE 12.21 8 293.2 179.1 10 -70 -26 d4-9-HODE 0.554 15-HpETE 12.21 8 335.2 113.1 10 -58 -20 d8-12-HETE UnCal

9(10)-EpODE 12.22 8 293.2 275.2 10 -60 -20 d4-12(13)-EpOME 0.164 17-HDoHE 12.22 8 343.3 281.2 10 -45 -20 d8-12-HETE 0.871 13-HpODE 12.41 8 311.2 179.1 10 -40 -20 d4-9-HODE UnCal


4


Compound tR (min)

Acquisition Period

Precursor (m/z)

Product (m/z)

Dwell Time (msec)

DCP (V)

CE (V)


12(13)-EpODE 12.42 8 293.2 183.2 10 -60 -25 d4-12(13)-EpOME 0.373 15-KETE 12.48 8 317.2 273.2 10 -65 -20 d8-12-HETE 4.98 11-HETE 12.57 8 319.2 167.1 30 -55 -15 d8-12-HETE 1.08

14(15)-EpETE 12.59 8 317.2 247.2 30 -45 -15 d4-12(13)-EpOME 0.493 9-KODE 12.66 8 293.2 185.2 10 -70 -30 d4-9-HODE 3.74

d8-12-HETE 12.71 8 327.2 184.2 10 -60 -21 CUDA IS 9-HpODE 12.79 8 311.2 185.2 10 -40 -20 d4-9-HODE UnCal 12-HETE 12.82 8 319.2 179.1 10 -60 -21 d8-12-HETE 2.01 8-HETE 12.96 8 319.2 155.1 10 -55 -21 d8-12-HETE 1.94

12-HpETE 12.98 8 335.2 153.1 10 -58 -20 d8-12-HETE UnCal 15-HETrE 13.02 8 321.2 221.2 10 -65 -24 d8-12-HETE 0.712 12-KETE 13.07 8 317.2 273.2 10 -65 -20 d8-12-HETE UnCal 9-HETE 13.16 8 319.2 167.1 30 -55 -15 d8-12-HETE 1.94

d8-5-HETE 13.4 8 327.2 116.1 10 -55 -22 CUDA IS 19(20)-EpDoPE 13.46 8 343.3 281.2 10 -45 -20 d4-12(13)-EpOME 1.67

5-HETE 13.5 8 319.2 257.2 / 115.1 10 -50 -20 d8-5-HETE 1.78 d4-12(13)-EpOME 13.57 8 299.2 198.1 10 -65 -25 CUDA IS

12(13)-EpOME 13.65 8 295.2 195.1 10 -65 -25 d4-12(13)-EpOME 0.375 14(15)-EpETrE 13.73 8 319.2 219.2 10 -70 -16 d4-12(13)-EpOME 4.61 16(17)-EpDoPE 13.84 8 343.5 273.5 30 -55 -15 d4-12(13)-EpOME 1.4 9(10)-EpOME 13.88 8 295.2 171.1 10 -60 -25 d4-12(13)-EpOME 0.359

5-HpETE 14.06 8 335.2 155.1 10 -58 -20 d8-12-HETE UnCal 5-KETE 14.16 8 317.2 203.2 50 -70 -25 d8-5-HETE 0.433

11(12)-EpETrE 14.2 8 319.2 167.1 30 -55 -15 d4-12(13)-EpOME 3.91 8(9)-EpETrE 14.4 8 319.2 155.1/ 167.1 10 -55 -15 d4-12(13)-EpOME 4.54


5

Table S4. Concentrations of plasma* and liver† triglycerides (TG) and total cholesterol (TC).

Lipid Parameters Control DHA1 DHA2 DHA3 P value

Plasma TG (mM) 1.11 ± 0.12 a 1.10 ± 0.12 ab 0.84 ± 0.09 ab 0.70 ± 0.07 b 0.015

Plasma TC (mM) 13.20 ± 0.54 ab 13.85 ± 0.47 a 11.16 ± 0.56 bc 9.45 ± 0.30 c <0.001

Liver TG (µg/mg) 102.62 ± 14.63 a 91.09 ± 11.53 a 55.18 ± 5.10 ab 45.61 ± 6.60 b 0.002

Liver TC (µg/mg) 7.15 ± 0.60 a 7.98 ± 0.57 a 5.21 ± 0.49 ab 3.80 ± 0.20 b <0.001

All data are represented as mean ± SEM, *: n=30/group for plasma TG and TC; † n=10/group for liver TG and TC. a,b,c Mean values with unlike letters were significantly different (p < 0.05).

6

Table S5. Changes in systolic and diastolic blood pressures (sBP and dBP), and heart rate (HR).

Control DHA1 DHA2 DHA3 P value*

Before After Before After Before After Before After

Change in sBP (mmHg) 98 ± 2 97 ± 2 a 100 ± 3 99 ± 2 a 99 ± 2 96 ± 2 a 101 ± 2 85 ± 2 b 0.002

Change in dBP (mmHg) 67 ± 2 72 ± 2 68 ± 4 73 ± 2 66 ± 2 72± 1 67 ± 3 62 ± 2 0.181

Change in HR (bpm) 551 ± 10 642 ± 9 560 ± 8 654 ± 8 558 ± 11 639 ± 9 543 ± 6 628 ± 10 0.965

All data are represented as mean ± SEM, n=12/group. *: p value of the ANOVA realized on changes between the beginning and the end of the experimental period. a,b Mean values with unlike letters were significantly different (p < 0.05).

7

Table S6. Plasma and liver levels of major polyunsaturated fatty acids (PUFA). LDLR-/- mice were given by

daily oral gavages (20 weeks) either oleic acid rich sunflower oil (Control group) or a mixture of oleic acid rich

sunflower oil and DHA rich tuna oil providing 0.1%, 1% or 2% of energy as DHA (DHA1, DHA2, and DHA3

groups respectively).

Control DHA1 DHA2 DHA3 P value

Plasma PUFA (% total FA)

18:2 n-6 10.79 ± 0.18 a 11.22 ± 0.36 ac 12.99 ± 0.30 bc 12.44 ± 0.16 c <0.001

18:3 n-3 0.16 ± 0.01 a 0.22 ± 0.01 b 0.25 ± 0.01 bc 0.26 ± 0.01 c <0.001

20:4 n-6 (AA) 15.84 ± 0.68 a 10.35 ± 0.62 ab 5.05 ± 0.23 bc 4.21 ± 0.11 c <0.001

20:5 n-3 (EPA) 0.30 ± 0.02 a 0.86 ± 0.08 ab 5.08 ± 0.40 bc 7.03 ± 0.36 c <0.001

22:6 n-3 (DHA) 3.57 ± 0.10 a 5.35 ± 0.30 ab 9.28 ± 0.51 bc 11.56 ± 0.75 c <0.001

Liver PUFA(% total FA)

18:2 n-6 7.29 ± 0.42 a 8.28 ± 0.41 ab 9.52 ± 0.50 bc 10.39 ± 0.42 c <0.001

18:3 n-3 0.33 ± 0.03 0.39 ± 0.04 0.38 ± 0.04 0.37 ± 0.05 ns

8

20:4 n-6 (AA) 3.09 ± 0.37 3.19 ± 0.71 1.98 ± 0.19 2.37 ± 0.28 ns

20:5 n-3 (EPA) 0.06 ± 0.01 a 0.27 ± 0.05 ab 1.83 ± 0.24 bc 2.94 ± 0.18 c <0.001

22:6 n-3 (DHA) 1.06 ± 0.17 a 2.23 ± 0.57 ab 5.37 ± 0.82 bc 8.97 ± 1.01 c <0.001

All data are represented as mean ± SEM, n=10/group. a,b,c Mean values with unlike letters were significantly different (p < 0.05).

9

Table S7. Plasma levels of PUFAs-derived oxylipins. LDLR-/- mice were given by daily oral gavages (20 weeks) either oleic acid rich

sunflower oil (Control group) or a mixture of oleic acid rich sunflower oil and DHA rich tuna oil providing 0.1%, 1% or 2% of energy as

DHA (DHA1, DHA2, and DHA3 groups respectively).

Control DHA1 DHA2 DHA3 P value

Plasma oxylipins (nM)

20:4 n-6 Epoxides 1761.20 ± 280.00 a 2230.11 ± 494.61 ab 762.87 ± 124.97 bc 694.79 ± 105.79 c <0.001

14(15)-EpETrE 783.00 ± 150.26 1123.97 ± 278.67 376.41 ± 69.31 372.99 ± 69.43 0.018

11(12)-EpETrE 751.40 ± 107.89 a 860.78 ± 172.11 a 302.14 ± 46.04 b 245.00 ± 30.74 b <0.001

8(9)-EpETrE 226.80 ± 24.46 a 245.67 ± 46.09 a 84.32 ± 11.69 b 76.80 ± 7.98 b <0.001

20:4 n-6 Diols 69.17 ± 3.48 a 62.05 ± 3.32 a 29.33 ± 4.70 b 23.16 ± 1.89 b <0.001

14,15-DiHETrE 1.62 ± 0.09 a 1.05 ± 0.07 ab 0.55 ± 0.04 bc 0.52 ± 0.03 c <0.001

11,12- DiHETrE 1.93 ± 0.10 a 1.30 ± 0.11 ab 0.70 ± 0.06 bc 0.56 ± 0.04 c <0.001

8,9- DiHETrE 6.69 ± 0.44 a 5.22 ± 0.47 ab 2.66 ± 0.22 bc 1.79 ± 0.14 c <0.001

5,6-DiHETrE 58.93 ± 3.55 a 54.48 ± 2.82 ab 25.43 ± 4.43 bc 20.29 ± 1.75 c <0.001

10

20:4 n-6 Alcohols 543.15 ± 72.52 a 457.30 ± 78.81 a 185.34 ± 17.30 b 165.04 ± 19.12 b <0.001

20-HETE 56.84 ± 4.23 a 39.58 ± 4.51 a 18.06 ± 2.79 b 12.29 ± 1.00 b <0.001

15- HETE 57.60 ± 7.65 a 42.18 ± 5.51 a 14.73 ± 2.24 b 10.33 ± 2.43 b <0.001

12- HETE 139.58 ± 42.36 162.04 ± 55.60 51.73 ± 8.81 66.94 ± 15.01 0.208

11- HETE 40.20 ± 4.21 a 30.67 ± 3.58 a 14.04 ± 1.30 b 10.98 ± 1.77 b <0.001

8-HETE 51.63 ± 4.25 a 38.58 ± 3.54 ab 16.53 ± 2.18 bc 10.80 ± 1.53 c <0.001

9- HETE 41.40 ± 4.71 a 31.11 ± 4.25 ab 17.43 ± 1.13 bc 15.22 ± 1.84 c <0.001

5- HETE 155.90 ± 13.91 a 113.14 ± 10.37 ab 52.82 ± 5.21 bc 38.48 ± 3.86 c <0.001

20:5 n-3 Epoxides 44.15 ± 8.81 a 253.83 ± 67.56 ab 782.00 ± 112.46 bc 1149.90 ± 187.19 c <0.001

17(18)-EpETE 25.99 ± 5.47 a 144.88 ± 36.74 ab 466.40 ± 65.88 bc 677.00 ± 107.74 c <0.001

14(15)-EpETE 18.15 ± 3.39 a 108.96 ± 30.90 ab 315.60 ± 47.09 bc 472.90 ± 80.04 c <0.001

20:5 n-3 Diols 4.09 ± 0.37 a 5.56 ± 0.49 ab 17.06 ± 0.86 bc 22.76 ± 1.55 c <0.001

17,18-DiHETE 2.68 ± 0.24 a 3.78 ± 0.46 ab 12.38 ± 0.65 bc 17.51 ± 1.29 c <0.001

14,15-DiHETE 1.42 ± 0.16 a 1.78 ± 0.18 a 4.68 ± 0.32 b 5.25 ± 0.39 b <0.001

20:5 n-3 Alcohols 11.25 ± 1.87 a 26.50 ± 5.13 ab 118.15 ± 13.40 bc 188.01 ± 17.28 c <0.001

15-HEPE 1.10 ± 0.20 a 2.88 ± 0.57 a 11.63 ± 1.52 b 13.43 ± 1.37 b <0.001

11

12-HEPE 6.07 ± 1.51 a 15.75 ± 3.83 ab 66.61 ± 10.98 bc 136.27 ± 17.76 c <0.001

5-HEPE 4.08 ± 0.62 a 7.86 ± 1.14 a 39.91 ± 4.82 b 38.31 ± 4.55 b <0.001

22:6 n-3 Epoxides 904.94 ± 129.08 a 2129.44 ± 433.65 ab 2030.90 ± 264.35 ab 2796.80 ± 363.25 b 0.002

19(20)-EpDPE 678.70 ± 100.07 a 1564.44 ± 293.96 a 1452.30 ± 199.17 a 2041.90 ± 240.45 b 0.002

16(17)-EpDPE 226.24 ± 31.23 a 565.00 ± 140.51 a 578.60 ± 75.60 b 754.90 ± 125.58 b 0.004

22:6 n-3 Diol (19,20-DiHDPA) 5.77 ± 0.33 a 9.24 ± 0.72 ab 13.73 ± 0.66 bc 16.85 ± 0.96 c <0.001

22:6 n-3 Alcohol (17-HDoHE) 21.48 ± 2.74 a 37.80 ± 5.46 a 58.91 ± 5.17 b 69.55 ± 6.45 b <0.001

20:3 n-6-Derived oxylipins 5.96 ± 2 a 6.73 ± 1.5 a 4.2 ± 1.6 ab 3.59 ± 1.6 b <0.001

18:2 n-6-Derived oxylipins

12(13)-EpOME 283 ± 110 453 ± 260 378 ± 140 321 ± 110 ns

9(10)-EpOME 285 ± 110 435 ± 230 373 ± 150 301 ± 97 ns

12,13-DiHOME 4.11 ± 0.77 3.94 ± 1.1 3.43 ± 0.93 3.71 ± 0.49 ns

9,10-DiHOME 33.4 ± 5.7 a 30.6 ± 7.2 a 23.6 ± 4.3 b 21.8 ± 4.5 b <0.001

13-HODE 344 ± 98 400 ± 140 337 ± 73 324 ± 110 ns

9-HODE 71.4 ± 19 73.6 ± 21 72.1 ± 13 62.5 ± 24 ns

18:3 n-3-Derived oxylipins

12

15(16)-EpODE 30.4 ± 11 54.6 ± 30 44.8 ± 20 37.3 ± 16 ns

12(13)-EpODE 3.04 ± 0.95 6.73 ± 4 5.62 ± 2.6 5.47 ± 1.9 <0.05

9(10)-EpODE 21.7 ± 11 41.6 ± 23 33.6 ± 13 28.6 ± 13 ns

9-HOTE 2.05 ± 0.69 2.27 ± 0.82 2.2 ± 0.57 1.89 ± 0.51 ns

13-HOTE 3.78 ± 1.6 3.61 ± 1.3 3.6 ± 1 4.06 ± 1 ns

All data are represented as mean ± SEM, n=10/group. a,b,c Mean values with unlike letters were significantly different (p < 0.05).

1

Table S8: Thromboxane and prostaglandin stability through sample processing a

Hydrolysis + SPE SPE Standards SSTD

TXB2 b 24.5 ± 6.4 % 101 ± 1.5 % 101 ± 0.77 % d4-TXB2

6-keto PGF1a 101 ± 0.92 % 102 ± 1.2 % 102 ± 0.61 % d4-6-keto PGF1a

PGE1 b ND 106 ± 3.6 % 101 ± 0.53 % d4-PGD2

PGE2 b ND 103 ± 1.6 % 99.5 ± 2 % “”

PGE3 b ND 100 ± 2.9 % 101 ± 0.92 % “”

PGD2 b ND 101 ± 1.6 % 99.2 ± 0.31 % “”

15-deoxy PGJ2 b ND 115 ± 6.2 % 96.1 ± 0.4 % “”

PGJ2/ δ 12-PGJ2 b ND 96.1 ± 2.7 % 98 ± 0.5 % “”

PGF2a / F2-IsoP 108 ± 0.81 % 105 ± 1.1 % 105 ± 1.1 % d4-PGF2a

a - A high level calibration standard was subjected to solid phase extraction with or without prior

exposure to base hydrolysis procedures and compared to triplicate analyses of calibration standards.

Results are means ± SD relative to theoretical concentrations with adjustment for surrogate recoveries.

b - As previously reported, the thromboxanes and beta-hydroxy-keto prostanoids are unstable under

alkali conditions.

1

Table S9: Fatty acid triol stability through sample processing a


20-hydroxy-LTB4 95.4 ± 1.3 % 93.8 ± 1.5 % 100 ± 0.93 % d4 6-keto PGF1a

9,12,13-TriHOME 116 ± 3.5 % 112 ± 3.9 % 98.7 ± 1.1 % d4-6-keto PGF1a

9,10,13-TriHOME 122 ± 1.7 % 119 ± 8 % 102 ± 2.7 % d4-6-keto PGF1a

11,12,15-THET 106 ± 2.5 % 92.6 ± 1.4 % 101 ± 1.3 % d4-PGF2a

Resolvin E1 92.1 ± 2.4 % 90.7 ± 0.28 % 101 ± 0.58 % d4-6-keto PGF1a

Resolvin D1 b 74.3 ± 7.4 % 52.5 ± 1.8 % 103 ± 1.2 % d4-PGF2a

Lipoxin A4 b 78.8 ± 2.9 % 61.5 ± 2 % 104 ± 1.3 % d4-PGF2a




b - Poor analyte recoveries suggest a reduced accuracy in results for these analytes using the current

surrogate spiking/tagging regimen. However, high precision suggests that treatment effects within the

study cohort should be robustly detectable.

6

Table S10: Fatty acid diol stability through sample processing a


20-carboxy-LTB4 82.2 ± 1.6 % 73.3 ± 1.4 % 97.4 ± 1.3 % d4 6-keto PGF1a

LTB4 92.8 ± 6.4 % 97.7 ± 13 % 92.2 ± 4.3 % d4-LTB4

6-trans-LTB4 88.3 ± 8.1 % 88.5 ± 9.9 % 94 ± 6.8 % d4-LTB4

LTB5 94.1 ± 6.9 % 103 ± 14 % 94.1 ± 5.7 % d4-PGF2a

8,15-DiHETE 99.6 ± 5.5 % 102 ± 4.9 % 95.4 ± 2.1 % d11-14,15-DiHETrE

5,15-DiHETE b 37.7 ± 1.7 % 107 ± 1.7 % 95.8 ± 0.33 % d11-14,15-DiHETrE

12,13-DiHOME 112 ± 12 % 135 ± 7.5 % 93.4 ± 0.53 % d11-14,15-DiHETrE

9,10-DiHOME 117 ± 10 % 134 ± 4.7 % 96.6 ± 1.2 % d11-14,15-DiHETrE

15,16-DiHODE 116 ± 9.6 % 133 ± 5.2 % 100 ± 0.1 % d11-14,15-DiHETrE

12,13-DiHODE 111 ± 9.7 % 131 ± 4.4 % 96.2 ± 0.56 % d11-14,15-DiHETrE

9,10-DiHODE 111 ± 11 % 132 ± 5.1 % 96.8 ± 0.92 % d11-14,15-DiHETrE

14,15-DiHETrE 95.1 ± 2.4 % 96.3 ± 0.92 % 94.9 ± 1.1 % d11-14,15-DiHETrE

11,12-DiHETrE 93.3 ± 2.9 % 93.1 ± 0.92 % 97.9 ± 2.7 % d11-14,15-DiHETrE

8,9-DiHETrE 103 ± 8.4 % 99.4 ± 0.31 % 110 ± 2.7 % d11-14,15-DiHETrE

5,6-DiHETrE 127 ± 2.3 % 78.4 ± 1.3 % 98.1 ± 0.31 % d11-14,15-DiHETrE

17,18-DiHETE 122 ± 1.6 % 111 ± 4.5 % 99 ± 5.4 % d11-14,15-DiHETrE

14,15-DiHETE 119 ± 5.5 % 108 ± 1.7 % 105 ± 2.3 % d11-14,15-DiHETrE

19,20-DiHDoPA 83.2 ± 4.3 % 72.2 ± 2.7 % 100 ± 0.23 % d11-14,15-DiHETrE




b - Poor analyte recoveries suggest a reduced accuracy in results for these analytes using the current

surrogate spiking/tagging regimen. However, high precision suggests that treatment effects within the

study cohort should be robustly detectable.

7

Figure S1. Correlations between doses of DHA given to LDLR-/- mice and plasma levels

of PUFA and of AA, EPA, and DHA and their corresponding oxylipins.

y = -4.5458x + 25.551

R² = 0.7609, ns

0

5

10

15

20

25

30

35

0.00 0.50 1.00 1.50 2.00

n-6

PU

FA

(%

to

tal

FA

)

Dose of DHA (% daily energy intake)

Plasma n-6 PUFA= f (dose of DHA)

y = -4.9714x + 12.713

R² = 0.7406, ns

0

2

4

6

8

10

12

14

16

18

0.00 0.50 1.00 1.50 2.00

AA

(%

to

tal F

A)


Plasma 20:4 n-6 (AA) = f (dose of DHA)

y = 3.4297x + 0.6589

R² = 0.9575, p<0.01

0

1

2

3

4

5

6

7

8

0.00 0.50 1.00 1.50 2.00

EP

A (

% t

ota

l F

A)


Plasma 20:5 n-3 (EPA) = f (dose of DHA)

y = 180.62x + 56.687

R² = 0.9882, p<0.01

0

200

400

600

800

1000

1200

1400

1600

1800

0.00 2.00 4.00 6.00 8.00

EP

A's

ox

yli

pin

s (n

M)

Plasma EPA (% total FA)

Plasma EPA's oxylipins = f (plasma EPA)

y = 191.29x + 600.84

R² = 0.7412, ns

0

500

1000

1500

2000

2500

3000

3500

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

DH

A's

ox

yli

pin

s (n

M)

Plasma DHA (% total FA)

Plasma DHA's oxylipins = f (plasma DHA)

y = 7.3529x + 5.5647

R² = 0.9523, p<0.01

0

5

10

15

20

25

0.00 0.50 1.00 1.50 2.00

n-3

PU

FA (

% t

ota

l FA

)


Plasma n-3 PUFA= f (dose of DHA)

y = 3.7845x + 4.5061

R² = 0.9408, p<0.01

0

2

4

6

8

10

12

14

0.00 0.50 1.00 1.50 2.00D

HA

(%

to

tal

FA

)Dose of DHA (% daily energy intake)

Plasma 22:6 n-3 (DHA) = f (dose of DHA)

y = 202.48x + 424.3

R² = 0.7931, p<0.05

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0.00 5.00 10.00 15.00 20.00

AA

's o

xyli

pin

s (n

M)

Plasma AA (% total FA)

Plasma AA's oxylipins = f (plasma AA)

8

Figure S2. Correlations between plasma levels of EPA, and their corresponding specific

oxylipins.

y = 90.559x + 28.184

R² = 0.9887

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

17

(18

)-E

pE

TE

(n

M)

Plasma [EPA] (% total FA)

Plasma 17(18)-EpETE= f (plasma EPA)

y = 62.103x + 22.909

R² = 0.9816

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

500.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

14

(15

)-E

pE

TE

(n

M)


Plasma 14(15)-EpETE= f (plasma EPA)

y = 2.1685x + 1.8945

R² = 0.9971

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

17

,18

-DiH

ET

E (

nM

)


Plasma 17,18-DiHETE= f (plasma EPA)

y = 0.5957x + 1.3044

R² = 0.9834

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

14

,15

-DiH

ET

E (

nM

)


Plasma 14,15-DiHETE= f (plasma EPA)

y = 1.8709x + 1.0548

R² = 0.9817

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

15

-HE

PE

(n

M)


Plasma 15-HEPE= f (plasma EPA)

y = 17.681x - 2.4713

R² = 0.9386

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.001

2-H

EP

E (

nM

)



y = 5.6468x + 3.8112

R² = 0.9231

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

5-H

EP

E (

nM

)



9

Figure S3. Correlations between plasma levels of DHA, and their corresponding specific

oxylipins.

y = 130.72x + 461.92

R² = 0.7085

0.00

500.00

1000.00

1500.00

2000.00

2500.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

19

(20

)-E

pD

PE

(nM

)

Plasma [DHA] (% total FA)

Plasma 19(20)-EpDPE = f (plasma DHA)

y = 53.372x + 134.15

R² = 0.7722

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

16

(17

)-Ep

DP

E (n

M)


Plasma 16(17)-EpDPE = f (plasma DHA)

y = 1.3357x + 1.4626

R² = 0.9912

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

19

,20

-DiH

PD

A (

nM

)


Plasma 19,20-DiHDPA = f (plasma DHA)

y = 5.8646x + 3.3083

R² = 0.9849

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

19

,20

-DiH

PD

A (

nM

)


Plasma 17-HDoHE = f (plasma DHA)

10

Figure S4. Correlations between doses of DHA given to LDLR-/- mice and liver levels of

PUFA and plasma levels of n-6 PUFA, n-3 PUFA and DHA and their corresponding

peroxidized metabolites.

y = 0.7571x + 11.599

R² = 0.6526, ns

0

2

4

6

8

10

12

14

16

0.000 0.500 1.000 1.500 2.000

n-6

PU

FA

(%

to

tal

FA

)


Liver n-6 PUFA= f (dose of DHA)

y = 5.4246x + 2.0757

R² = 0.9909, p<0.01

0

2

4

6

8

10

12

14

16

0.000 0.500 1.000 1.500 2.000

n-3

PU

FA

(%

to

tal

FA

)


Liver n-3 PUFA= f (dose of DHA)

y = -0.4578x + 3.0118

R² = 0.5421, ns

0

1

1

2

2

3

3

4

4

5

0.000 0.500 1.000 1.500 2.000

AA

(%

to

tal

FA

)


Liver 20:4 n-6 (AA) = f (dose of DHA)

y = 1.4484x + 0.1528

R² = 0.9866, p<0.01

0

1

1

2

2

3

3

4

0.000 0.500 1.000 1.500 2.000

EP

A (

% t

ota

l F

A)


Liver 20:5 n-3 (EPA) = f (dose of DHA)

y = 3.7809x + 1.4768

R² = 0.9912, p<0.01

0

2

4

6

8

10

12

0.000 0.500 1.000 1.500 2.000

DH

A (

% t

ota

l F

A)


Liver 22:6 n-3 (DHA) = f (dose of DHA)

y = -4.731x + 75.323

R² = 0.5232, ns

0

5

10

15

20

25

30

35

11.00 11.50 12.00 12.50 13.00 13.50

HN

E (

pm

ole

/m

g p

rote

in)

Liver n-6 PUFA (% total FA)

Liver HNE = f (liver n-6 PUFA)

y = 0.2492x + 5.5607

R² = 0.7579, ns

0

1

2

3

4

5

6

7

8

9

10

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

HH

E (

pm

ole

/m

g p

rote

in)

Liver n-3 PUFA (% total FA)

Liver HHE = f (liver n-3 PUFA)

y = 0.1715x - 0.0991

R² = 0.9946, p<0.01

0

0

0

1

1

1

1

1

2

2

0.00 2.00 4.00 6.00 8.00 10.00

F4

-NP

(p

mo

le/

mg

pro

tein

)

Liver DHA (% total FA)

Liver F4-Neuroprostanes = f (liver DHA)

11

Figure S5. Partial least squares discrimination analysis of dietary groups in LDLR-/-

given by daily oral gavages increasing doses of DHA. A) Animals eating each dietary

mixture with complete data sets (n=10/group) were segregated by PLS analyses. B) Group

segregation was driven by plasma (yellow triangle) and liver (orange triangle) metabolites

belonging to clusters C2, C3, and C4 as identified in Figure 7. The plasma EPA metabolite,

17,18-dihydroxyeicosaotetraenoic acid (17,18-DiHETE) and the liver DHA metabolite group

F4-neuroprostanes (F4-NeuroPs) were the most frequently selected variables occurring in

80% and 60% of models, respectively.

6

Figure S6. Correlation between plasma F2-isoprostanes and 9-HETE levels.

y = 0.0022x + 0.1237R2 = 0.0236

0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40 50 60

9-HETE

PG

F2A

/F2-

Iso

Ps

PGF2a / (isoprostanes)

6

Figure S7. Correlations between plasma oxylipins and plasma 9-HETE levels.

y = 4.33x - 12.20

R2 = 0.33

y = 1.57x - 10.24

R2 = 0.87

y = 11.03x + 359.94

R2 = 0.09

y = 0.03x + 0.17

R2 = 0.67

y = 1.00x - 2.22

R2 = 0.94

0

500

1000

1500

2000

2500

0 20 40 60 80

Plasma 9-HETE (nM)

Pla

sma

Oxy

lipin

s (n

M)

11-HETE

12-HETE

15-HETE

14(15)-EpETrE

14,15-DiHETrE

Lipid Profiling following Intake of the Omega 3 Fatty Acid DHA Identifies the Peroxidized...

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