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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: Prostaglandins Leukot Essent Fatty Acids. 2021 Nov 1;175:102360. doi: 10.1016/j.plefa.2021.102360

SPM Pathway Marker Analysis of the Brains of Obese Mice in the Absence and Presence of Eicosapentaenoic Acid Ethyl Esters

Matthew Vander Ploeg *,#, Kevin Quinn †,#, Michael Armstrong †, Jonathan Manke †, Nichole Reisdorph †, Saame Raza Shaikh *
PMCID: PMC8633202  NIHMSID: NIHMS1754267  PMID: 34743051

Abstract

Obesity drives an imbalanced signature of specialized pro-resolving mediators (SPM). Herein, we investigated if high fat diet-induced obesity dysregulates the concentration of SPM intermediates in the brains of C57BL/6J mice. Furthermore, given the benefits of EPA for cardiometabolic diseases, major depression, and cognition, we probed the effect of an EPA supplemented high fat diet on brain SPM intermediates. Mass spectrometry revealed no effect of the high fat diet on PUFA-derived brain metabolites. EPA also did not have an effect on most brain PUFA-derived metabolites except an increase of 12-hydroxyeicosapentaenoic acid (12-HEPE). In contrast, EPA dramatically increased serum HEPEs and lowered several PUFA-derived metabolites. Finally, untargeted mass spectrometry showed no effects of the high fat diet, with or without EPA, on the brain metabolome. Collectively, these results show the murine brain resists a deficiency in SPM pathway markers in response to a high fat diet and that EPA supplementation increases 12-HEPE levels.

INTRODUCTION

The increasing incidence of obesity, which is over 40% in adults in the United States alone, contributes toward the pathophysiology of many complications. Examples of complications include hypertension, type 2 diabetes, neurodegeneration, cardiovascular diseases, liver steatosis, and even increased risk of infections. A common feature across these metabolic diseases is a failure to resolve inflammation (1), which is a viable therapeutic target to improve physiological outcomes in individuals with obesity. Notably, the resolution of inflammation is governed, in part, by metabolites of the specialized pro-resolving mediator (SPM) family (2). SPMs known as resolvins, protectins, and maresins, are enzymatically synthesized from the n-3 polyunsaturated fatty acids (PUFA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (2). SPMs known as lipoxins are also generated from the n-6 PUFA arachidonic acid (2). SPMs are produced during the transition to resolution of inflammation and they drive damaged tissue to homeostasis through the activation of key signaling pathways (2). SPMs are distinct from immunosuppressive therapies and offer new therapeutic opportunities for the treatment of inflammatory diseases (3).

There is strong evidence from murine and human studies that metabolites of the SPM family are decreased with obesity in several tissues, which has consequences for inflammatory, infectious, and metabolic outcomes (1,4,5). As an example, we previously showed that C57BL/6J mice fed a high fat diet, relative to lean controls, displayed a strong reduction in the resolvin E1 (RvE1) pathway marker 18-hydroxyeicosapentaenoic acid (18-HEPE) in white adipose tissue and liver (6). There is also evidence that DHA-derived SPM pathway markers are decreased with obesity and its co-morbidities (7,8). For instance, the SPM precursor 17-hydroxydocosahexaenoic acid (17-HDHA) is decreased in white adipose tissue and spleens of obese/diabetic mice compared to controls and moreover, administration of this metabolite improves infectious outcomes (9–11). A recent study also found that 17-HDHA was decreased in humans with obesity, which was driven by impaired activity of the SPM generating 15-lipoxygenase (12). Overall, these studies establish that obesity and its associated co-morbidities such as type 2 diabetes, are generally driving a dysregulated signature of SPMs and their intermediates across tissues, which is likely contributing toward uncontrolled inflammation.

The primary objective of this study was to determine if obesity drives an SPM pathway marker deficiency in the brain. Furthermore, we investigated if long term administration of EPA ethyl esters could potentially increase SPM pathway markers. The rationale for studying EPA was driven by our previous work to show that EPA improved hyperinsulinemia and hyperglycemia, in part, through the targeting of the RvE1-ChemR23 axis (6). Finally, we followed up with untargeted mass spectrometry based metabolomic analyses to further investigate if specific metabolic pathways are being dysregulated by a high fat diet in the absence or presence of EPA.

MATERIALS AND METHODS

2.1. Mouse model.

C57BL/6J male mice were administered experimental diets as previously described (6). Briefly, mice that were 5–6 weeks of age were fed for 15 weeks either a lean control diet (10% kcal lard, Envigo TD.160407), a high fat diet (60% kcal lard, Envigo TD.06414), or a high fat diet supplemented with EPA (Envigo TD.160232). EPA accounted for 2% of total energy and was administered as ethyl esters (Cayman Chemical). All experiments adhered to strict IACUC guidelines for euthanasia and humane treatment in addition to the NIH Guide for the Care and Use of Laboratory Animals. Euthanasia relied on CO2 inhalation followed by cervical dislocation.

2.2. Lipidomic analyses using targeted mass spectrometry.

The brain was immediately snap-frozen in liquid nitrogen upon sacrifice. A range of n-3 and n-6 PUFA-derived metabolites were analyses using a mass spectrometry based approach as previously described in extensive details (6,13). Briefly, metabolites were extracted using Strata-X 33-μm 30 mg/1 mL SPE columns. Quantitation was performed using two-dimensional reverse phase HPLC tandem mass spectrometry (liquid chromatography/tandem mass spectrometry). Rigorous standards were employed for the LC/MS/MS analysis and were purchased from Cayman Chemical (13). All solvents for mass spectrometry studies were HPLC grade (Fisher Scientific) or better.

2.3. Metabolomic analyses with mass spectrometry.

Metabolomic analyses were as previously described (6). Samples were analyzed using liquid chromatography/mass spectrometry (LC/MS) and raw data were extracted and processed using Agilent Technologies MassHunter Profinder Version B.10.0 SR2 (Profinder) software in combination with Agilent Technologies Mass Profiler Professional Version 15.1 (MPP) as previously shown (6). The data was normalized in MPP using ‘Total Abundance’ in all samples (also referred to a Total useful Signal), and then baselined using ‘baseline to median of all samples’ (also referred to as Centering the data) to reduce bias caused by higher abundant compounds when generating principal component analysis (PCA) and hierarchical clusters.

2.4. Analyses.

Targeted lipidomic data were analyzed using Graph Pad Prism Version 9.1.2. If the data set satisfied the assumptions of normality and homogeneity of variance tested by the Shapiro-Wilks test and Bartlett test, respectively, then we relied on one-way ANOVAs followed by a post-hoc Tukey test. We then determined statistically significant metabolites between the experimental groups and the controls. One of the samples from the high fat diet group (known as HF_105) in the targeted analysis was an outlier from all the other samples and was excluded from analyses. We then calculated fold changes. Metabolomics data were analyzed using MPP. Clustering of data was visualized between the dietary groups with PCA using all metabolites. PCA was performed on experimental groupings with pruning option selection of 4 principal components. Data was further visualized using hierarchical clustering on both experimental treatment and metabolites using Euclidean Similarity Measure and Wards Linkage Rule. Statistical analysis was performed using one-way ANOVA and multiple testing correction with Benjamini Hochberg FDR. For all lipidomic and metabolomic analyses, P < 0.05 was considered statistically significant.

RESULTS

3.1. Analysis of brain PUFA-derived metabolites of obese mice in the absence or presence of EPA ethyl esters.

We previously reported the metabolic profile of these mice (6). The mice on a high fat diet were glucose intolerant, hyperinsulinemic, and hyperglycemic. Mice consuming EPA displayed significant improvements in glucose tolerance, hyperinsulinemia, and hyperglycemia relative to the high fat diet (6).

Targeted mass spectrometry analyses revealed that a high fat diet in the absence or presence of EPA ethyl esters, relative to a lean control, had no impact on the concentration of metabolites from n-6 PUFAs in the brain (Figure 1). The concentration of HETEs (Figure 1A), EETs (Figure 1B), leukotrienes (Figure 1C, left panel), lipoxin A4 (Figure 1C, right panel), thromboxanes (Figure 1D), and prostaglandins (Figure 1E) in the brain were the same for all three diet groups. We also analyzed select metabolites from the n-6 PUFA linoleic acid in the brain (Supplemental Figure 1). The concentration of these metabolites was also the same across the diet groups.

Figure 1: Analysis of brain arachidonic acid-derived metabolites in response to a high fat diet in the absence and presence of EPA ethyl esters.

Figure 1:

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The concentration of arachidonic acid-derived (A) HETEs, (B) EETs, (C) leukotrienes and LXA4, (D) thromboxanes, and (E) prostaglandins. N.D.: not detectable. N = 5 mice per diet. Data are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 by a one-way ANOVA with Tukey’s Multiple Comparison Test.

There was no effect of the high fat diet, compared to the lean controls, on brain metabolites synthesized from DHA (Figure 2A) or EPA (Figure 2B). EPA supplementation also did not impact brain DHA-derived metabolites (Figure 2A). However, EPA supplementation of a high fat diet increased the concentration of 12-HEPE compared to the lean control and high fat diet by nearly 10-fold in the brain, but did not influence the levels of 18-HEPE (Figure 2B).

Figure 2: Analysis of brain n-3 PUFA-derived metabolites in response to a high fat diet in the absence and presence of EPA ethyl esters.

Figure 2:

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The concentration of (A) DHA-derived and (B) EPA-derived metabolites. N.D.: not detectable. N = 5 mice per diet. Data are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 by a one-way ANOVA with Tukey’s Multiple Comparison Test.

3.2. Analysis of circulating PUFA-derived metabolites of obese mice in the absence or presence of EPA ethyl esters.

We next compared the results on brain with circulating levels of PUFA-derived metabolites. The high fat diet had no effect on the levels of HETEs (Figure 3A), EETs (Figure 3B), leukotrienes (Figure 3C, left panel), lipoxin A4 (Figure 3C, right panel), thromboxanes (Figure 3D), and prostaglandins (Figure 3E) in comparison to the lean control diet. Administration of EPA lowered the concentration of 5-HETE, 8-HETE, and 12-HETE (Figure 3A) by up to 2-fold. EPA also lowered the concentration of TXA2 analog by 3.5-fold (Figure 3D) and PGI2 analog by 2.6-fold (Figure 3E). We also analyzed select metabolites from linoleic acid (Supplemental Figure 2). The high fat diet had no effect on any of the circulating HODEs; however, EPA lowered the concentration of 13-HODE relative to the high fat diet (Supplemental Figure 2).

Figure 3: Analysis of serum arachidonic acid-derived metabolites in response to a high fat diet in the absence and presence of EPA ethyl esters.

Figure 3:

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The concentration of arachidonic acid-derived (A) HETEs, (B) EETs, (C) leukotrienes and LXA4, (D) thromboxanes, and (E) prostaglandins. N.D.: not detectable. N = 5 mice per diet. Data are mean ± SEM. N = 5 mice per diet. Data are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 by a one-way ANOVA with Tukey’s Multiple Comparison Test.

EPA supplementation of a high fat diet did not influence the concentration of DHA-derived metabolites (Figure 4A). However, EPA supplementation of a high fat diet increased the levels of 12-HEPE and 18-HEPE dramatically compared to the control and high fat diets (Figure 4B). In fact, the levels of 18-HEPE were elevated by about 7000-fold (Figure 4B).

Figure 4: Analysis of serum n-3 PUFA-derived metabolites in response to a high fat diet in the absence and presence of EPA ethyl esters.

Figure 4:

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The concentration of (A) DHA-derived and (B) EPA-derived metabolites. N.D.: not detectable. N = 5 mice per diet. Data are mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 by a one-way ANOVA with Tukey’s Multiple Comparison Test.

3.3. Metabolomic analyses of the brains from obese mice in the absence and presence of EPA.

We next used mass spectrometry to determine if EPA, in particular, has any effect on the brain metabolome. We focused on the lipophilic, methyl tertbutyl ether soluble, metabolites. In total, 1268 metabolites were detected in at least 2 brain samples after blank subtraction. The data were visualized using PCA on these metabolites; no distinct separation based on experimental treatment was observed, particularly between the high fat diet in the absence and presence of EPA (Figure 5A). Hierarchical clustering (HC) was performed to determine whether there was clustering among the groups and/or metabolites (Figure 5B). Following a one-way ANOVA, no metabolites were found to be statistically significant (FDR < 0.05). HC was consistent with the ANOVA and PCA results; that is, samples did not cluster with treatment.

Figure 5: Metabolomic analysis of brain metabolites in response to a high fat diet in the absence and presence of EPA ethyl esters.

Figure 5:

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(A) Principal component analysis (PCA) of brain samples generated from metabolites detected in at least 2 brain samples showing distribution of samples for control (red squares), high fat (blue circles), and high fat + EPA (yellow triangles) diets. Colored circles around each of the three groups are the 95% confidence ellipses showing that variances in sample data is not due to treatment. (B) Hierarchical clustering (HC) of the samples, y axis, (red = control, blue = high fat, yellow = high fat + EPA) and detected metabolites, x axis. Each metabolite is represented by a line and its color represents the median abundance on a log2 transformed scale, either yellow (0 = median), red (above the median to 11.7), or blue (below the median to −11.7). No treatment specific clustering was observed in either the PCA or HC.

DISCUSSION

SPMs have a critical role in the resolution of inflammation including within the brain (14). Previous studies show that obesity and its associated co-morbidities generally drive tissue-specific SPM intermediate deficiencies, relative to controls (6–8,12,15–18). Replenishing these intermediates, either with the use of dietary n-3 PUFAs or direct administration of SPM intermediates or SPMs, has benefits for differing complications of obesity including insulin resistance, hepatic steatosis, adipose tissue inflammation, and even the response to viral infections (6,9,10,19–23). Furthermore, there is mounting evidence that SPMs improve outcomes related to inflammation with stress, depression, Alzheimer’s Disease, traumatic brain injury, and peripheral nerve injury (24–29). Therefore, this study investigated if diet-induced obesity was driving a deficiency in the concentration of SPMs and their precursors in the brains of C57BL/6J mice.

There are several reasons why we may not have detected changes in the lipophilic metabolites or SPM pathway markers such as 14-HDHA, 17-HDHA, and 18-HEPE in response to a high fat diet. One possibility is that we conducted whole brain analyses, which may not reflect more subtle localized changes. For instance, in humans, body mass index is strongly associated with inflammation in the hypothalamus, dorsal striatum, thalamus, fornix, anterior limb of the internal capsule, and posterior thalamic radiation (30). Thus, future studies will need to examine SPM and lipophilic metabolite levels in specific regions of the brain. A second limitation is that we did not study if there are measurable SPM deficiencies in the brains of obese mice with in situ fixation, thus; subtle changes in the brain metabolome may be masked by the sample preparation. In addition, the timing of intervention, which was 15 weeks with the high fat diet, may not be long enough to drive an inflammatory profile and thereby elicit a change in metabolites of the SPM pathway. Therefore, future studies will need to investigate how differing periods of intervention with a high fat diet, particularly in the context of aging, may drive SPM deficiencies. Given evidence of sex-differences in SPM levels (16,31,32), there is also a need to evaluate SPM levels in the brains of female mice. Moreover, a brain inflammation model may be better suited to drive a change in the concentration of SPMs.

We focused on the impact of EPA ethyl esters on brain metabolites. There are several reasons for studying EPA. First, we and others have reported improved glucose tolerance, hyperinsulinemia, hyperglycemia, and inflammation upon EPA supplementation of a high fat diet (6,23,33,34). In addition, we also found EPA could reverse some of the changes in key transcripts driven by a high fat diet within the hypothalamic-pituitary axis (35). Furthermore, there is compelling evidence that EPA can exert potential benefits for major depression, potentially through the generation of downstream metabolites (36). There is even a potential role for EPA on cognitive outcomes in healthy adults. For instance, a clinical trial with healthy adults showed that EPA-enriched oils, unlike those enriched in DHA, improved global cognitive function (37).

We found a significant increase in 12-HEPE in the brains of obese mice upon EPA intervention. This result was consistent with research that identified 12-HEPE as the major lipoxygenase product in response to [14C]-EPA treatment of brains cells isolated from rainbow trout (38). More work is clearly need with 12-HEPE as this metabolite may have an important role in the brains of obese mice. 12-HEPE has garnered attention in the context of obesity as it was identified as a batokine that improves glucose uptake into target tissues such as skeletal muscle and adipose tissue (39).

In circulation, we observed 12-HEPE and 18-HEPE were elevated with EPA; in addition, EPA, unlike the brain, also lowered several PUFA-derived metabolites, notably HETEs. This is an area of study that warrants further investigation as HETEs may be a therapeutic target for differing brain-related injuries. As an example, 20-HETE levels are increased in plasma of individuals that have experienced traumatic brain injury and have poor neurological outcomes (40). Furthermore, pharmacological inhibition of 20-HETE improves neuroinflammation and functional endpoints in a rodent model of brain injury (41). The increase in 18-HEPE suggests potential activation of the RvE1-ChemR23 axis, consistent with our work and of others to show that resolvin E1 has an important role in controlling metabolic outcomes (6,42,43). Although we did not detect RvE1, it is entirely plausible that the metabolite underwent rapid conversion to an inactive product, as demonstrated for resolvin D1 (9,15).

In summary, the data show that the brain, unlike other metabolic tissues, is resilient to a change in the concentration of SPM intermediates in response to administration of a high fat diet for 15 weeks to male mice. The resilience may not be permanent and future studies will need to determine if continued consumption of an obesogenic diet with age leads to deficiencies of SPM pathway markers, particularly in select regions of the brain. In addition, the work shows that EPA, which is increasingly appreciated to have potential benefits for cardiometabolic outcomes, major depression, and cognition, leads to a significant increase in 12-HEPE. These results warrant future functional and mechanistic studies on the role of 12-HEPE in the brain.

Supplementary Material

1

Highlights.

  • The effect of obesity on brain SPM intermediates is unknown

  • High fat diet does not lower the levels of murine brain SPM intermediates

  • Eicosapentaenoic acid (EPA) supplementation of a high fat diet increases brain 12-HEPE

  • EPA supplementation also lowers select circulating n-6 PUFA-derived metabolites

Acknowledgments

This work was supported by NIH R01AT008375 (S.R.S.), NIH P30DK056350 (S.R.S.), NIH S10 RR026522–01 (N.R.).

Abbreviations:

DHA

docosahexaenoic acid

EPA

eicosapentaenoic acid

PUFA

polyunsaturated fatty acid

SPM

specialized pro-resolving mediators

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of interest: S.R.S. has previously received corporate support for studies focused on omega-3 fatty acids. S.R.S. is also currently supported by the industry for organizing national/international conferences related to diet and inflammation.

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