MS-Based Lipidomics Approach To Characterize Lipid

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UPLC-QqQ/MS-based lipidomics approach to characterize lipid alterations in inflammatory macrophages Jae Won Lee, Hyuck Jun Mok, Dae-Young Lee, Seung Cheol Park, Geum-Soog Kim, Seung-Eun Lee, Young-Seob Lee, Kwang Pyo Kim, and Hyung Don Kim J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00848 • Publication Date (Web): 02 Mar 2017 Downloaded from http://pubs.acs.org on March 3, 2017

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Journal of Proteome Research

UPLC-QqQ/MS-based lipidomics approach to characterize lipid alterations in inflammatory macrophages Jae Won Leea, Hyuck Jun Moka, Dae Young Leeb, Seung Cheol Parka, Geum-Soog Kimb, Seung-Eun Leeb, Young-Seob Leeb, Kwang Pyo Kim*a, and Hyung Don Kim*b,c

a

Department of Applied Chemistry, The Institute of Natural Science, College of Applied Science,

Kyung Hee University, Yongin 17104, Republic of Korea b

Department of Herbal Crop Research, National Institute of Horticultural and Herbal Science,

RDA, Eumseong 27709, Republic of Korea. c

Department of Biochemistry, School of Life Sciences, Chungbuk National University,

Cheongju 28644, Republic of Korea

* Corresponding author: Hyung Don Kim Phone: 82-43-871-5614; Fax: 82-43-871-5589; E-mail: [email protected] * Co-corresponding author: Kwang Pyo Kim Phone: 82-31-201-2419; Fax: 82-31-201-2340; E-mail: [email protected]

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Abstract In this study, UPLC-QqQ/MS-based lipidomics was applied to profile various lipids from RAW264.7 macrophages treated with different concentrations of lipopolysaccharide (LPS). The degree of inflammation increased with the LPS concentration. To elucidate the altered lipid metabolism of inflammatory macrophages, we targeted to analyze 25 lipid classes from LPS-treated RAW264.7 cells. As a result, 523 lipid species were successfully profiled by using the optimal UPLC and MRM. Statistical data analyses such as PCA, PLS-DA, and HCA differentiated five RAW264.7 cells treated with different concentrations of LPS. VIP plot, heat map, and bar-plot also provided lists of up- or down-regulated lipids according to the LPS concentration. From the results, 11 classes of lipids—TG, DG, ChE, PE, PS, PI, PA, LyPC, LyPE, Cer, and dCer—were increased, and three classes—cholesterol, PC, and LyPA—were decreased in an LPS concentration-dependent manner. Furthermore, the treatment of an anti-inflammatory compound recovered the levels of PC, PE, PI, PA, LyPE, LyPA, and Cer from the activated macrophages. Finally, these results demonstrate the correlation between inflammation and lipid metabolism in macrophages. The differentially regulated lipids also have the potential to be used as biomarkers for inflammation.

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Keywords: Lipidomics, inflammation, macrophages, lipopolysaccharide, UPLC-QqQ/MS

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Introduction Inflammation which has critical roles in immunological processes, is associated with numerous human conditions and diseases1, 2. Excessive or inappropriate inflammation can contribute to cause acute and chronic human diseases including type 2 diabetes, atherosclerosis, cancer, etc3-6. Thus, inflammation has been widely studied to identify its roles in several diseases7-9. Inappropriate or excessive production of inflammation mediators including eicosanoids and cytokines is a common characteristic of inflammation 10. In particular, prostaglandin E2 (PGE2) is an eicosanoid that exhibits pro-inflammatory actions11. Arachidonic acid (AA), a major fatty acid, serves as the substrate for synthesis of several eicosanoids including PGE2 in inflammatory cells12. A previous review indicated that inflammation and lipid signaling are intertwined modulators of homeostasis and immunity13. Not only eicosanoids and inositol phospholipids but many other lipid species function to regulate inflammatory responses both positively and negatively14-16. Conversely, inflammatory signaling can also significantly affect lipid metabolism in the liver, adipose tissue, and macrophages17-19. Furthermore, injurious stimuli (i.e. infection and inflammation) can induce the acute-phase response and lead to multiple alterations in the

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metabolism of lipids and lipoproteins20. Thus, it is critical to perform a detailed characterization of the altered lipid metabolism depending on inflammation. Macrophages typically respond to infection and inflammation via biosynthesis of various eicosanoids21. Many studies have assessed the altered profiles of eicosanoids, which have proand anti-inflammatory activities in macrophages22,

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. The impacts of inflammation on the

secretion of pro-inflammatory cytokines and gene expression have been evaluated in a variety of cell types24. However, much less is known regarding the influence of inflammation on altered lipid metabolism. Although Rouzer et al. performed phospholipid profiling of inflammatory macrophages25, 26, many other lipids remain to be analyzed. In cells, lipids have critical roles as structural membrane components, sources of energy, and signaling molecules. As hormones and cellular messengers, lipids are involved in many intracellular and intercellular signaling processes27. Lipids also adjust membrane fluidity and support diverse cellular processes28. Thus, it is critical to determine the lipid metabolism correlated with inflammation. Recently, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) has been used as an effective tool to analyze various lipids from biological samples29-31. An ultra-performance LC (UPLC) enables the rapid and effective separation of individual lipid species32. Multiple reaction monitoring (MRM) based on triple quadrupole (QqQ)/MS is also 5



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applicable for the sensitive analysis of target compounds33, 34. Optimal lipidomics based on UPLC-QqQ/MS has been widely applied to shed light on many biological events35-37. In our previous study, we performed eicosanoid profiling in RAW264.7 macrophages treated with lipopolysaccharide (LPS) to characterize the metabolism of eicosanoids correlated with inflammation38. However, the profiles of lipids altered by inflammation remain to be determined. In this study, UPLC-QqQ/MS was thus used to analyze various lipids from RAW264.7 cells treated with different concentrations of LPS. We also treated an anti-inflammatory compound to the activated macrophages, and assessed the altered levels of lipids. The detailed characterization of lipids in inflammatory macrophages may provide a good understanding of the lipid metabolism correlated with inflammation.

Experimental procedures Reagents HPLC-grade methanol, acetonitrile, water, and 2-propanol were purchased from J.T. Baker (Avantor Performance Material, Inc., Center Valley, PA, USA). Chloroform and ammonium formate were purchased from Sigma-Aldrich (St. Louis, MO, USA). HPLC-grade formic acid 6


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was purchased from Fluka Analytical, Sigma Aldrich Chemie GmbH (Steinheim, Germany). Lipid standards such as PC (10:0-10:0), PC (12:0-12:0), PE (10:0-10:0), PS (10:0-10:0), PG (10:0-10:0), PI (8:0-8:0), PA (10:0-10:0), LyPC (13:0), LyPE (14:0), LyPS (17:1), LyPG (14:0), LyPI (13:0), LyPA (14:0), SM (d18:1-12:0), Cer (d18:1-12:0), dCer (d18:0-12:0), So (d17:1), Sa (d17:0), Cer1P (d18:1-12:0), So1P (d17:1), and Sa1P (d17:0) were purchased from Avanti Polar Lipids, Inc. TG (11:1-11:1-11:1), DG (8:0-8:0), MG (15:1), ChE (10:0), and cholesterol were purchased from Larodan Fine Chemicals AB (Malmö, Sweden). Trimethylsilyldiazomethane (TMSD) was purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), trypsin–EDTA, and penicillin–streptomycin were purchased from HyClone Laboratories Inc. (Logan, UT, USA). Griess reagent, E. coli LPS, and rosiglitazone were purchased from Sigma Chemical Co. (St Louis, MO, USA).

Cell culture and the treatment of LPS and rosiglitazone Murine RAW264.7 macrophages (KCLB 40071) were obtained from the Korean Cell Line Bank (KCLB; Seoul, South Korea) and grown in cell culture media (DMEM containing 10% heat-inactivated FBS, 100 U/mL penicillin, and 100 µg/mL streptomycin) at 37°C in a 7



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humidiﬁed atmosphere with 5% CO2. Cells were seeded in a 100-mm culture dish at a concentration of 100,000/mL, cultured for 24 h, and incubated for a further 18 h after treatment with 100 µL LPS (0, 1, 10, 100, and 1000 ng/mL). Rosiglitazone (50µM) was pretreated for 1 h before LPS stimulation. Pellets containing 1 × 107 cells were collected from each culture dish and used for analyses. Cells were washed with 10 mL PBS (Phosphate Buffered Saline) 3 times before cell seeding and harvesting to eliminate background lipid signature.

Nitric oxide and ELISA analysis A spectrophotometric assay based on the Griess reaction was used to measure the concentrations of nitric oxide (NO) in the culture supernatants. Sodium nitrite of known concentrations (0–100 µM) was used to construct a standard curve. The level of TNF-α was analyzed by using commercial ELISA kits (BD Biosciences, San Diego, CA, USA), in accordance with the manufacturer’s instructions.

Sample preparation Each lipid standard was dissolved in chloroform/methanol (1:1, v/v) and stored at –20°C before analyses. It was then diluted to the desired concentration for use. In the extraction of cellular 8


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lipids, a two-step method including neutral and acidic extractions was used39. In the neutral extraction, the cell pellet was added into the 1 mL of chloroform/methanol (1:2, v/v) with internal standards (ISs) (1 µg/mL of TG (11:1-11:1-11:1), DG (8:0-8:0), MG (15:1), ChE (10:0), PC (10:0-10:0), PE (10:0-10:0), PS (10:0-10:0), PG (10:0-10:0), PI (8:0-8:0), PA (10:0-10:0), LyPC (13:0), LyPE (14:0), LyPS (17:1), LyPG (14:0), LyPI (13:0), LyPA (14:0), SM (d18:1-12:0), Cer (d18:1-12:0), dCer (d18:0-12:0), So (d17:1), Sa (d17:0), Cer1P (d18:1-12:0), So1P (d17:1), and Sa1P (d17:0)). The sample was incubated for 10 min on ice. After centrifugation (13,800 × g, 2 min at 4°C), the 950 µL of supernatant was transferred to a new tube. Next, in the acidic extraction, the 750 µL of chloroform/methanol/37% (1N) HCl (40:80:1, v/v/v) was added into remaining pellet, and incubated for 15 min at room temperature. And then, the 250 µL of cold chloroform and the 450 µL of cold 0.1 M HCl were added into the sample followed by 1 min of vortexing and centrifugation (6,500 × g, 2 min at 4°C). The bottom organic phase was collected, and pooled with a prior extract. Subsequently, the sample was equally divided into two aliquots and dried using a SpeedVac concentrator. One was then dissolved in the 50 µL of methanol for the TMSD methylation and acidic lipid analyses, and the other was dissolved in the 50 µL of solvent A/solvent B (2:1, v/v) for neutral and basic lipid analyses.

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TMSD methylation It has been known that TMSD has toxicity and its inhalation can cause central nervous system depression, drowsiness, dizziness, and lung damage. Furthermore, extreme care is needed to handle this reagent although it is not explosive like diazomethane. Thus, we used adequate safety equipments to perform TMSD methylation in a fume hood. A solution of TMSD (2 mol/L) in hexane was added to the cellular lipid extracts dissolved in methanol, and then the color of solution was changed to yellow. After vortexing for 30 s, methylation was performed at 37°C for 15 min. Glacial acetic acid was added to quench the reaction of methylation and yielded colorless samples. The samples were then subjected to LC/MS analysis33.

UPLC-QqQ/MS conditions The 6490 Accurate-Mass Triple Quadrupole LC-MS coupled to a 1200 series HPLC system (Agilent Technologies, Wilmington, DE, USA) was used for the lipid profiling. The used column was a Hypersil GOLD column (2.1 × 100 mm ID; 1.9 µm, Thermo Science). The temperatures of the sample tray and column oven were set to 4°C and 40°C, respectively. Mobile phases consisted of solvent A (acetonitrile/methanol/water (19:19:2, v/v/v) + 20 mmol/L ammonium formate + 0.1% (v/v) formic acid) and solvent B (2-propanol + 20 mmol/L ammonium formate + 10


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0.1% (v/v) formic acid). The gradient elution program was as follows: 0–5 min, B 5%; 5–15 min, B 5–30%; 15–22 min, B 30–90%; 22–25 min, B 90%; 25–26 min, 90–5%; and 26–30 min, B 5%. The flow rate was 250 µL/min, and total run time was 30 min. The volume of sample injection was 2 µL for each run. The parameters of operating source conditions were as follows: 3,500 V positive mode of capillary voltage, 3,000 V negative mode of capillary voltage, sheath gas flow of 11 L/min (UHP nitrogen) at 200°C, drying gas flow of 15 L/min at 150°C, and nebulizer gas flow at 25 psi. The optimal MRM conditions were used to analyze various lipid species.

Quality control (QC) analysis A mixture of TG (11:1-11:1-11:1), PC (10:0-10:0), LyPC (13:0), and SM (d18:1-12:0) was prepared as a QC sample. Intra- and inter-day precision and accuracy were investigated by QC analysis, performed 15 replicates (n=15). The precision of LC/MS analysis was determined by coefficient of variation (CV) not exceeding 14%.

Data processing and statistical analysis Agilent Mass Hunter Workstation Data Acquisition software was used to process the LC/MS data. Qualitative Analysis B.06.00 software (Agilent Technologies, Wilmington, DE, USA) was used 11



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to export the m/z of precursor ions, m/z of product ions, and retention time (RT) of target lipids in the MRM data. Next, an in-house database constructed by using Skyline software package (MacCoss Laboratory, University of Washington, Seattle, WA, USA) was applied to calculate the assigned lipid’s peak area in the raw data. For the statistical analyses, MetaboAnalyst website40 was used to carry out principal component analysis (PCA), projection to latent structure discriminant analysis (PLS-DA), and hierarchical cluster analysis (HCA).

Results and discussion Inflammation induced murine macrophages Macrophages are cells that constitute a major part of the response to infection and inflammation. The RAW264.7 macrophage line is widely used as a model for primary macrophages41. For the mechanistic study of inflammation, LPS is also widely used as a well-characterized inducer of cytokines and the acute-phase response42. In this study, LPS-stimulated mouse RAW264.7 macrophages were used to characterize detail the lipid metabolism correlated with inflammation. Per the experiments in our previous study38, the cells were treated with 0, 1, 10, 100, and 1000 ng/mL LPS for 18 h, and the supernatants were collected. To estimate the degree of inflammation 12


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in five samples, they were subjected to NO quantification and measurement of pro-inflammatory cytokine levels. The results showed that NO content and the secretion of tumor necrosis factor-α (TNF-α), a pro-inflammatory cytokine, were increased in a concentration-dependent manner (Figure S1). These results showed that LPS well activated inflammatory signaling in mouse RAW264.7 macrophages. Furthermore, LPS-treated RAW264.7 cells were appropriate for evaluating the altered lipid metabolism by inflammation.

Lipid profiling of RAW264.7 cells treated with different concentrations of LPS Next, five samples of RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS were subjected to lipidomic analysis. For the profiling of various lipids from inflammatory RAW264.7 cells, we applied a previously constructed analytical method using UPLC-QqQ/MS35, 36. First, various lipid standards including TG, DG, MG, ChE, cholesterol, PC, PE, PS, PG, PI, PA, LyPC, LyPE, LyPS, LyPG, LyPI, LyPA, SM, Cer, dCer, So, Sa, Cer1P, So1P, and Sa1P were used to optimize their MRM conditions. TMSD methylation was applied to sensitively analyze several highly acidic lipids, namely, PS, PI, PA, LyPS, LyPI, LyPA, Cer1P, So1P, and Sa1P33. MRM transition and MS/MS collision energy were optimized to profile 25 lipid classes (Table S-1). The performance of this method was validated in our previous study36. To prove the 13



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reproducibility and reliability of the lipid profiling method, we list the validation data in Table S-2. For the profiling of RAW264.7 cells, 24 standards, namely, TG (11:1-11:1-11:1), DG (8:0-8:0), MG (15:1), ChE (10:0), PC (10:0-10:0), PE (10:0-10:0), PS (10:0-10:0), PG (10:0-10:0), PI (8:0-8:0), PA (10:0-10:0), LyPC (13:0), LyPE (14:0), LyPS (17:1), LyPG (14:0), LyPI (13:0), LyPA (14:0), SM (d18:1-12:0), Cer (d18:1-12:0), dCer (d18:0-12:0), So (d17:1), Sa (d17:0), Cer1P (d18:1-12:0), So1P (d17:1), and Sa1P (d17:0), were used as the ISs. An octadecylsilyl silica column was used to separate various lipid species according to their total carbon chain length (Cn) and total degree of unsaturation (Un). According to the MRM data, the RT of each lipid was increased with a higher Cn and a lower Un. Each peak was assigned based on a reasonable RT in the data processing. Finally, in the RAW264.7 cells, a total of 523 lipid species were successfully analyzed as follows: 38 TGs, 41 DGs, 11 MGs, 19 ChEs, cholesterol, 82 PCs, 29 PEs, 47 PSs, 17 PGs, 22 PIs, 47 PAs, 19 LyPCs, 15 LyPEs, 18 LyPSs, 11 LyPGs, 14 LyPIs, 18 LyPAs, 12 SMs, 19 Cers, 6 dCers, 2 So, Sa, 30 Cer1Ps, 2 So1Ps, and 2 Sa1Ps (Table S-3). In the data processing, each lipid’s peak area in the MRM data was calculated by using Skyline software with an in-house library. Peak area of IS was then used to normalize the peak 14


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area of individual lipid species. For example, TG (11:1-11:1-11:1) was used to normalize individual TG species. Other standards were also applied for the normalization of each lipid class. In particular, ChE (10:0) was used to normalize cholesterol. Finally, the normalized datasets were subjected to statistical analysis.

Statistical analysis of lipid profiles from RAW264.7 cells treated with different concentrations of LPS The UPLC-QqQ/MS-based lipidomic approach was applied to profile various lipids from RAW264.7 cells treated with different concentrations of LPS (0, 1, 10, 100, and 1000 ng/mL). Assays were carried out in triplicate (n=3) for each concentration of LPS. Then, we statistically analyzed the lipid profiles obtained from five sample groups, namely, control (0 ng/mL LPS), LPS1 (1 ng/mL LPS), LPS10 (10 ng/mL LPS), LPS100 (100 ng/mL LPS), and LPS1000 (1000 ng/mL LPS). First, PCA was performed to visualize the general clustering trends of the five groups. As a result, the five groups were well differentiated in the PCA score plot (Figure 1). This represents analyses that described 99.4% of the total variance including principal component 1 (98.3%) and principal component 2 (1.1%), where principal component 1 was the major component for discrimination. Controls were scattered on the lower side of the plot. LPS1, 15



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LPS10, LPS100, and LPS1000 were scattered on the upper side in this order. Second, the datasets were subjected to PLS-DA to classify the phenotypes of samples and identify the differentiating lipids. In the PLS-DA score plot, the five groups were also well separated (Figure 2A). The PLS-DA score plots described 87.3% of the total variance including Component 1 (65.3%) and Component 2 (22%). Controls were scattered on the left side of the plot. Furthermore, LPS1, LPS10, LPS100, and LPS1000 were scattered on the right side in this order. Figure 2B also presents the variable importance in projection (VIP) plot of 25 lipids (VIP scores Top 25) that were differently regulated among the five samples. The individual lipid species PC, PE, LPC, LPE, LPS, LPI, LPA, and ChE were altered mainly depending on the concentration of LPS. Third, we performed HCA of the acquired lipid datasets from the five samples. In the HCA, the datasets of control, LPS1, LPS10, LPS100, and LPS1000 were well distinguished (Figure 3A). The alteration of 100 lipids according to LPS concentration (VIP scores Top 100) was also described in a heat map (Figure 3B). The levels of 53 lipids belonging to ChE, DG, Cer, and dCer were increased by LPS treatment in a concentration-dependent manner. In contrast, 11 Cer1Ps were decreased with increasing LPS concentration. Other lipids were not altered in a concentration-dependent manner. 16


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The statistical analyses indicated that the lipid profiles of RAW264.7 cells differ depending on the LPS concentration. Higher LPS concentrations induced more severe inflammation. LPS concentration-altered lipids are likely strongly associated with inflammation. Lipids have critical roles in the survival and functions of cells. Thus, detailed characterization of lipids from inflammatory RAW264.7 cells can offer a deeper understanding about the macrophage lipid metabolism correlated with inflammation.

Detailed characterization of altered lipids in inflammatory macrophage cells To determine lipid alterations in inflammatory macrophages, using the normalized datasets, various lipids from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS were quantified. Each lipid class contains various species. In MS, it is practically limited to quantify whole lipid species because it is not available to use all the lipid standards. MS based lipidomics has this limitation nowadays. Thus, as the semi-quantification, the normalized areas of individual lipids were summed to determine the amount of each lipid class. Finally, we semi-quantified the total amount of 25 lipid classes from macrophages. Next, bar-plots were constructed to represent the lipid alterations depending on the LPS concentration. First, we showed the bar-plots for five neutral lipids (TG, DG, MG, ChE, and 17



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cholesterol) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS (Figure 4). TG that typically has roles in energy storage in cells43,

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was up-regulated in an LPS

concentration-dependent manner. DG and MG were also up-regulated in the LPS-treated cells. DG is associated with the biosynthesis of TG and phospholipids45. Diacylglycerol acyltransferase diverts DG from membrane phospholipid synthesis to TG synthesis. This is also able to up-regulate TG. It has been previously known that LPS-stimulated macrophages store more TG and ChE46. Our results of TG and ChE profiling were well matched with the previous report. We also represented the altered levels of DG and MG in the activated macrophages. Interestingly, cholesterol was down-regulated and ChE up-regulated in an LPS concentration-dependent manner. Cholesterol, an essential component of cellular membranes, is critical as a precursor for synthesizing steroid hormones and bile acids47. Lecithin cholesterol acyltransferase (LCAT) converts free cholesterol into ChE, which is the major form to transport and storage cholesterol in lipoprotein and most cell types48. Based on the results of down-regulated cholesterol and up-regulated ChE, we expected that LCAT may have functions in LPS-induced inflammatory mechanisms of macrophages. To validate this hypothesis, semi-quantitative RT-PCR and immunoblot were performed to measure the gene expression of LCAT in the activated macrophages. However, LCAT were not detected from the macrophages (data not shown). 18


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Finally, we found that an unknown mechanism which is not related to LCAT regulated the levels of cholesterol and ChE. Second, the bar-plots of six phospholipids (PC, PE, PS, PG, PI, PA) and six lysophospholipids (LyPC, LyPE, LyPS, LyPG, LyPI, LyPA) are presented in Figure 5. In cells, phospholipids and lysophospholipids have roles as membrane components and in signal transduction49,

50

. Phospholipids, especially PC, are also sources of AAs that serve as the

precursors for synthesizing the lipid mediators such as various eicosanoids25, 26. In this study, PC was down-regulated and LyPC up-regulated in an LPS concentration-dependent manner. This indicated that AA released from PC was used as the precursor for eicosanoids, and PC was converted into LyPC. Furthermore, the levels of PE, PS, PI, PA, and LyPE were increased and LyPA decreased in the inflammatory cells. However, other lipids were not regulated in an LPS concentration-dependent manner. From our results, the alteration of PC and PE might have been associated with phosphatidylethanolamine N-methyltransferase, an enzyme that converts PE to PC51. Furthermore, LyPA is esterified mainly into PA by 1-acylglycerol-3-phosphate acyltransferase (AGPAT). Thus, AGPAT might have caused the altered levels of PA and LyPA in the inflammatory cells. Phospholipase D also hydrolyzes phospholipids to release PA and an alcohol52. Our previous study represented that inflammatory RAW264.7 cells were differentiated 19



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with spreading and pseudopodial formation38. Thus, the profiling of phospholipids related to membrane components should be applicable to selecting target enzymes that have critical roles in the morphological changes of inflammatory macrophages. Third, the bar-plots of eight sphingolipids (SM, Cer, dCer, So, Sa, Cer1P, So1P, Sa1P) and their metabolic pathway are presented in Figure 6. In sphingolipid metabolism, Sa is acylated to form dCer, and dCer is desaturated to form Cer. Subsequently, Cer can be converted into SM, Cer1P, and So. Furthermore, Sa and So can be converted into Sa1P and So1P, respectively. Among these

eight

sphingolipids,

dCer and

Cer were

up-regulated

in

an

LPS

concentration-dependent manner. On the other hand, other lipids showed irregular patterns. Cer has roles as a membrane component and in cellular signaling, such as differentiation, proliferation, and apoptosis of cells53. When 1 ng/mL LPS was used to treat RAW264.7 cells, the level of SM was increased compared with that in the control. However, SM was down-regulated when the concentration of LPS was increased. This indicated that LPS-induced inflammation is associated with sphingomyelinase, which hydrolyzes SM into Cer. The alterations of Cer1P, So1P, and Sa1P were also related to the behavior of kinases that catalyze the transfer of phosphate from phosphate-donating molecules to specific substrates. The comprehensive profiling of sphingolipids has the potential to reveal the correlation between inflammation and 20


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cellular signaling in macrophages.

Effect of rosiglitazone on the lipid metabolism in inflammatory macrophage cells In this study, by the profiling of 25 lipid classes from macrophages treated with different LPS concentrations, we found that 12 lipid classes (i.e. TG, DG, MG, ChE, PE, PS, PI, PA, LyPC, LyPE, Cer, dCer) were increased, and 3 lipid classes (i.e. cholesterol, PC, LyPA) were decreased in an LPS concentration-dependent manner. This indicated that the up- or down-regulated lipids can be the potential candidates for inflammation-related lipids. To validate if these 15 lipid classes are correlated with inflammation, we additionally performed the experiments by using a rosiglitazone that belongs to the family of thiazolidinedione. The anti-inflammatory effects of rosiglitazone has been previously characterized54. By the test of NO levels, we confirmed that rosiglitazone has an anti-inflammatory effect (data not shown). We then performed the target profiling of 15 lipid classes from the RAW264.7 cells treated with LPS or LPS + rosiglitazone. By the data processing, we semi-quantified the total amount of 15 lipid classes from the two samples. And then, bar-plots were constructed to represent the lipid alterations depending on the rosiglitazone treatment (Figure 7). As a result, among the 15 lipid classes, the levels of 7 classes (i.e. PC, PE, PI, PA, LyPE, LyPA, Cer) were recovered by the rosiglitazone treatment. This 21



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indicated that the 7 lipid classes were correlated with LPS-induced inflammation and the anti-inflammatory activity of rosiglitazone. These lipids are classified as phospholipids (i.e. PC, PE, PI, PA), lysophospholipids (i.e. LyPE, LyPA), and sphingolipid (Cer). In the biological system, they mainly function to construct cellular membrane and in lipid signaling. The altered metabolism of these lipids may function in the activated macrophages.

Conclusions A UPLC-QqQ/MS-based lipidomic analysis characterized the lipid metabolism of inflammatory macrophages. RAW264.7 macrophages were treated with different LPS concentrations, and the inflammatory degree increased with higher concentration of LPS. Lipid profiling was used to analyze 523 individual species from 25 lipid classes successfully. This is a result of profiling the largest number of individual lipid species from macrophages. In the statistical analyses, five groups of RAW264.7 cells treated with different amounts of LPS were well differentiated by PCA, PLS-DA, and HCA. VIP plot and heat map were also used to identify the up- or down-regulated lipids. The bar-plots of 25 lipid classes showed alterations in neutral lipids, phospholipids, lysophospholipids, and sphingolipids by LPS concentration. This study firstly assessed the altered levels of macrophage lipids depending on the degree of inflammation. The 22


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levels of 15 lipid classes were differentially regulated in an LPS concentration-dependent manner. Moreover, the levels of 7 lipid classes were recovered by the rosiglitazone treatment. Our lipidomic approach has the potential to reveal correlations between lipid metabolism and inflammation. The differentially regulated lipids by LPS and rosiglitazone are the potential candidates as biomarkers for inflammation. In future, it is required to analyze the inflammation related lipids to study its roles in cellular functioning and pathophysiological events.

Acknowledgements This work was conducted with the support of the "Cooperative Research Program for Agriculture Science & Technology Development” (Project No. PJ01135001), Rural Development Administration, Republic of Korea.

Supporting Information Table S1. Optimal MRM conditions for 25 lipids. Table S2. Validation of 25 lipids profiling based on MRM and the LODs of each lipid standard. 23



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Table S3. The list of 523 lipids analyzed from the inflammatory macrophages by using UPLC/QqQ-MS. Figure S1. (A) Nitric oxide (NO) and (B) tumor necrosis factor-α (TNF-α) by RAW264.7 cells treated with 0, 1, 10, 100, and 1,000 ng/mL LPS. *, p value < 0.05; ***, p value < 0.001.

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Figure legends Figure 1. Principal component analysis score plot of lipid profiles obtained from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL lipopolysaccharide (LPS). Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. Figure 2. (A) The projection to latent structure discriminant analysis score plot of lipid profiles obtained from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. (B) The variable importance in projection (VIP) plot of 25 lipids (VIP scores Top 25) that were differently regulated among the five samples. Figure 3. (A) Hierarchical cluster analysis of lipid datasets from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. (B) The heat map of 100 lipids (VIP scores Top 100) from the five samples. Figure 4. The bar-plots of five neutral lipids (TG, DG, MG, ChE, cholesterol) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. TG, triacylglycerol; DG, diacylglycerol; MG, monoacylglycerol; ChE, cholesterylester. Figure 5. The bar-plots of six phospholipids (PC, PE, PS, PG, PI, PA) and six lysophospholipids 29



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(LyPC, LyPE, LyPS, LyPG, LyPI, LyPA) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000

ng/mL

LPS.

PC,

phosphatidylcholine;

PE,

phosphatidylethanolamine;

PS,

phosphatidylserine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PA, phosphatidic acid; LyPC,

lysophosphatidylcholine;

LyPE,

lysophosphatidylethanolamine;

LyPS,

lysophosphatidylserine; LyPG, lysophosphatidylglycerol; LyPI, lysophosphatidylinositol; LyPA, lysophosphatidic acid. Figure 6. The bar-plots and metabolic pathway of eight sphingolipids (SM, Cer, dCer, So, Sa, Cer1P, So1P, Sa1P) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. SM, sphingomyelin; Cer, ceramide; dCer, dihydroceramide; So, sphingosine; Sa, sphinganine; Cer1P, ceramide-1-phosphate; So1P, sphingosine-1-phosphate; Sa1P, sphinganine-1-phosphate. Figure 7. The bar-plots of PC, PE, PI, PA, LyPE, LyPA, and Cer from RAW264.7 cells treated with LPS or LPS + rosiglitazone (Rosi). *, p value < 0.05; **, p value < 0.01.

30


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Figure 1. Principal component analysis score plot of lipid profiles obtained from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL lipopolysaccharide (LPS). Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. 163x157mm (300 x 300 DPI)



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Figure 2. (A) The projection to latent structure discriminant analysis score plot of lipid profiles obtained from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. (B) The variable importance in projection (VIP) plot of 25 lipids (VIP scores Top 25) that were differently regulated among the five samples. 91x49mm (300 x 300 DPI)


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Figure 3. (A) Hierarchical cluster analysis of lipid datasets from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. Con, 0 ng/mL LPS; LPS1, 1 ng/mL LPS; LPS10, 10 ng/mL LPS; LPS100, 100 ng/mL LPS; LPS1000, 1000 ng/mL LPS. (B) The heat map of 100 lipids (VIP scores Top 100) from the five samples. 335x399mm (96 x 96 DPI)



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Figure 4. The bar-plots of five neutral lipids (TG, DG, MG, ChE, cholesterol) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. TG, triacylglycerol; DG, diacylglycerol; MG, monoacylglycerol; ChE, cholesterylester. 74x68mm (300 x 300 DPI)


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Figure 5. The bar-plots of six phospholipids (PC, PE, PS, PG, PI, PA) and six lysophospholipids (LyPC, LyPE, LyPS, LyPG, LyPI, LyPA) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. PC, phosphatidylcholine; PE, phosphatidylethanolamine; PS, phosphatidylserine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PA, phosphatidic acid; LyPC, lysophosphatidylcholine; LyPE, lysophosphatidylethanolamine; LyPS, lysophosphatidylserine; LyPG, lysophosphatidylglycerol; LyPI, lysophosphatidylinositol; LyPA, lysophosphatidic acid. 86x44mm (300 x 300 DPI)



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Figure 6. The bar-plots and metabolic pathway of eight sphingolipids (SM, Cer, dCer, So, Sa, Cer1P, So1P, Sa1P) from RAW264.7 cells treated with 0, 1, 10, 100, and 1000 ng/mL LPS. SM, sphingomyelin; Cer, ceramide; dCer, dihydroceramide; So, sphingosine; Sa, sphinganine; Cer1P, ceramide-1-phosphate; So1P, sphingosine-1-phosphate; Sa1P, sphinganine-1-phosphate. 105x65mm (300 x 300 DPI)


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Figure 7. The bar-plots of PC, PE, PI, PA, LyPE, LyPA, and Cer from RAW264.7 cells treated with LPS or LPS + rosiglitazone (Rosi). *, p value < 0.05; **, p value < 0.01. 73x51mm (300 x 300 DPI)



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FOR TOC ONLY 41x22mm (300 x 300 DPI)


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MS-Based Lipidomics Approach To Characterize Lipid

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