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. 2019 May 23;9:7776. doi: 10.1038/s41598-019-43240-4

Multi-omics Analysis of Liver Infiltrating Macrophages Following Ethanol Consumption

John O Marentette 1,#, Meng Wang 1,#, Cole R Michel 1, Roger Powell 1, Xing Zhang 1, Nichole Reisdorph 1, Kristofer S Fritz 1,✉, Cynthia Ju 1,✉
PMCID: PMC6533323  PMID: 31123328

Abstract

Alcoholic liver disease (ALD) is a significant health hazard and economic burden affecting approximately 10 million people in the United States. ALD stems from the production of toxic-reactive metabolites, oxidative stress and fat accumulation in hepatocytes which ultimately results in hepatocyte death promoting hepatitis and fibrosis deposition. Monocyte-derived infiltrating Ly6Chi and Ly6Clow macrophages are instrumental in perpetuating and resolving the hepatitis and fibrosis associated with ALD pathogenesis. In the present study we isolated liver infiltrating macrophages from mice on an ethanol diet and subjected them to metabolomic and proteomic analysis to provide a broad assessment of the cellular metabolite and protein differences between infiltrating macrophage phenotypes. We identified numerous differentially regulated metabolites and proteins between Ly6Chi and Ly6Clow macrophages. Bioinformatic analysis for pathway enrichment of the differentially regulated metabolites showed a significant number of metabolites involved in the processes of glycerophospholipid metabolism, arachidonic acid metabolism and phospholipid biosynthesis. From analysis of the infiltrating macrophage proteome, we observed a significant enrichment in the biological processes of antigen presentation, actin polymerization and organization, phagocytosis and apoptotic regulation. The data presented herein could yield exciting new research avenues for the analysis of signaling pathways regulating macrophage polarization in ALD.

Subject terms: Metabolomics, Proteomics, Monocytes and macrophages

Introduction

Alcoholic liver disease (ALD) affects approximately 10 million people in the United States and is a significant economic burden and public health hazard1. The pathogenesis of ALD stems from the production of toxic-reactive metabolites, reactive oxygen and nitrogen species (ROS and RNS), and oxidative stress associated with the metabolism of ethanol in hepatocytes2. Fat accumulation in hepatocytes (steatosis) is the earliest histopathological change in the liver associated with alcohol intake3. Continued steatosis results in hepatocyte death via apoptosis and necrosis which promotes inflammation and fibrosis formation4,5. A large number of individuals who develop fatty liver suffer no further complications while others progress from steatosis to hepatitis (liver inflammation). Persistent hepatitis and hepatocyte death can result in scar formation in the liver (cirrhosis) resulting in impaired liver function and altered architecture6. Persistent cirrhosis can ultimately lead to hepatocellular carcinoma and liver failure7.

Macrophages are instrumental in promoting and resolving the hepatitis and fibrosis associated with ALD as evidenced by clinical observations that macrophage inflammatory genes are upregulated in ALD and cirrhosis patients8. Furthermore, hepatic macrophage activation and enhanced production of tumor necrosis factor α (TNFα), interleukin (IL)-6, chemokine (C-C motif) ligand 2 (CCL2) and ROS is elicited with ethanol administration in ALD animals9,10. Kupffer cells (KC), the liver resident macrophages, account for approximately 90% of the macrophage population in the healthy liver11. KC are primarily involved in the maintenance of tissue homeostasis by serving as immune sentinels sensing pathogens, antigens or damaged cells through interactions with numerous cell surface receptors to initiate and potentiate the inflammatory response12. The immune response to liver injury is initiated through the production of pro-inflammatory cytokines, IL-1β and TNFα by KC. Additionally, KC produce chemokines, such as CCL2, which induces the recruitment of additional inflammatory cells, such as monocytes, to the site of injury13. Inflammation progresses with the chemotactic recruitment of Ly6C+ monocytes to inflamed tissue that differentiate into Ly6Chi infiltrating macrophages (IMs)13. During acute or chronic liver injury, the macrophage subtype promoting inflammation in the liver are Ly6Chi monocyte-derived macrophages14,15. Ly6Chi macrophages exert pro-inflammatory, tissue-destructive responses as well as releasing pro-fibrotic mediators, such as IL-1β, platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF) and transforming growth factor (TGF) β which activate hepatic stellate cells to deposit extracellular matrix and stimulate fibrosis formation16–19. While Ly6Chi macrophages initially exert pro-fibrotic and pro-inflammatory function they can differentiate into Ly6Clow macrophages to facilitate tissue repair and inflammation resolution20,21.

Macrophages represent an incredibly diverse cell type which, depending on tissue micro-environmental cues, switch from a pro- to anti-inflammatory phenotype in the progression of various diseases. The remarkable heterogeneity of macrophages is exemplified by their often opposing roles in a variety of diseases. For instance, pro-inflammatory macrophages are important in the elimination of extracellular pathogens, but are instrumental in the pathogenesis of atherosclerosis, autoimmune and metabolic diseases22. Anti-inflammatory macrophages are instrumental in wound healing and inflammation resolution but when not properly regulated, factor into the pathogenesis of asthma, fibrosis and cancer development23,24. During the progression of ALD, macrophages actively promote and resolve the inflammatory response, rendering therapeutic targeting of macrophages a significant challenge. Therefore, a thorough analysis of the metabolic and protein differences between Ly6Chi and Ly6Clow infiltrating macrophages following ethanol consumption is imperative in understanding the signaling pathways governing macrophage phenotypic switching. This mechanism could be harnessed for targeted therapeutic manipulation of macrophage populations in the liver.

In the current study, we isolated Ly6Chi and Ly6Clow macrophages from the livers of ethanol-fed mice and subjected the isolated cells to metabolomic and proteomic analysis to achieve an integrated bioinformatics approach. Here, we present an in-depth analysis of the altered metabolome and proteome between Ly6Chi and Ly6Clow liver infiltrating macrophages following ethanol consumption. The data herein elucidates novel signaling mechanisms governing macrophage phenotypic switching, with the potential for opening new avenues for therapeutic targeting macrophage polarization in ameliorating ALD progression.

Results

Comparative Metabolomic Analysis of Ly6Chi and Ly6Clow Infiltrating Macrophages Following Ethanol Administration

Infiltrating Ly6Chi and Ly6Clow liver macrophage populations from ethanol fed mice were isolated by flow sorting (Fig. 1). Following macrophage isolation, metabolites were separated from proteins using cold methanol extraction. Following methyl-tert-butyl ether (MTBE) liquid-liquid extraction, metabolites were analyzed by mass spectrometry (Fig. 2). After performing statistical analysis of the peak height intensities in Mass Profiler Professional, the ANOVA significant metabolites were uploaded to Metaboanalyst. We identified a number of metabolites with significant fold change differences between the Ly6Chi and Ly6Clow macrophages (Fig. 3). From the metabolite analysis, we observed 102 significantly altered metabolites between the macrophage subtypes (Table 1). In the lipid positive fraction, we detected 58 differentially regulated metabolites with 39 upregulated and 19 downregulated in the Ly6Clow compared to the Ly6Chi macrophages. From the lipid negative fraction, we measured 30 differentially regulated metabolites with 15 upregulated and 15 downregulated in the Ly6Clow compared to the Ly6Chi macrophages. In the aqueous fraction, we detected 14 differentially regulated metabolites with 8 being upregulated and 6 downregulated in Ly6Clow compared to the Ly6Chi macrophages. Following analysis with Metaboanalyst, we performed Metabolites Biological Role (MBROLE) analysis for pathway enrichment. From the 102 significantly altered metabolites we observed 6 pathways significantly enriched of which glycerophospholipid metabolism, arachidonic acid metabolism and phospholipid biosynthesis were further analyzed for their potential role in regulating macrophage polarization. (Table 2). Ly6Chi and Ly6Clow macrophages are significantly enriched for glycerophospholipid metabolism, metabolic pathways, arachidonic acid metabolism, linoleic metabolism and phospholipid biosynthesis with differential regulation of the metabolites involved in each functional pathway (Supplementary Table S1).

Figure 1.

Figure 1

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Liver macrophage flow sorting schematic. CD45 was used to select for myeloid cells. CD11b and SiglecF were used to gate out eosinophils (Eos, CD11b+ SiglecF+) from macrophages (Mϕ, CD11b+ SiglecF−). Macrophages F4/80 and CD11b were used to identify infiltrating macrophages (IM, CD11bhi F4/80Int) from Kupffer cells (KC, CD11bInt F4/80hi). Mixture of V450 conjugated anti-Ly6G, CD3, CD19, NK1.1 were used to gated out the neutrophils, lymophocytes and Nature Killer cells. IM were finally separated into the two infiltrating macrophage phenotypes based on expression level of Ly6C: Ly6Chi and Ly6Clow.

Figure 2.

Figure 2

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Liver macrophage metabolomics and proteomics sample preparation.

Figure 3.

Figure 3

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Significantly alter metabolites between Ly6Chi and Ly6Clow macrophages. (A) Log2 fold change of significantly altered metabolites (n = 3 in each experiment). The pink dots represent the significant metabolites. (B) Heat map of significantly altered metabolites (n = 3 in each experiment). Metabolites are significant with a fold change +/− 1.5 and t-test p < 0.05 when comparing Ly6Chi and Ly6Clow.

Table 1.

Significantly altered metabolites between Ly6Chi and Ly6Clow macrophages.

Compound p(Low vs High) Regulation Fold Change Mass Retention Time Metabolite ID
Lipid Positive Metabolites
17-Hydroxyprogesterone 1.1479999 1.92E-09 Up 52550.8 330.2258 1.1480 C01176
MG(0:0/18:1/0:0) 4.13E-07 Up 13307.73 378.2748 2.5390 C01885
Prosafrinine 0.01746852 Up 5.11865 305.234 0.7990 LMSP01080051
Cyclopassifloside V 0.006721366 Up 4.075243 882.4456 1.3970 HMDB35947
Okadaic acid 0.005180489 Up 2.907953 848.4317 1.4090 C01945
5,8,11-Eicosatrienoic acid 0.020084225 Up 2.815632 306.2565 1.7910 HMDB10378
5,8,11-Eicosatrienoic acid Esi + 1.7910002 0.03348608 Up 2.535191 306.2554 1.7910 HMDB10378
Spectinomycin adenylate 0.017278904 Up 2.418216 683.1959 6.5740 C03580
CL(20:4/20:4/18:1/18:1) 0.022371477 Up 2.385773 1501.0281 5.2100 C05980
9R-(2-cyclopentenyl)-1-nonanol 1.4750003 0.01784647 Up 2.252583 232.1784 1.4750 LMFA05000040
CL(16:0/18:1/18:1/18:0) 0.029356718 Up 2.216448 1433.0435 5.2100 C05980
PA(18:3/18:3) 0.044420037 Up 2.105103 692.4487 3.3660 LMGP10010015
Resiniferatoxin 0.001578898 Up 2.102412 628.2697 1.4160 C09179
Narasin 0.030150319 Up 2.018768 786.5013 1.0990 HMDB30448
(R)-1-O-[b-D-Glucopyranosyl-(1–6)-b-D-glucopyranoside]-1,3-octanediol 0.008763756 Up 1.982221 470.2431 2.0950 HMDB32799
PI(16:1/0:0) 0.0099774 Up 1.978465 570.2801 1.3970 LMGP06050009
Mephentermine 0.04304003 Up 1.732651 163.1362 0.4740 C07889
2-Hexyl-4,5-dimethyloxazole 0.039262477 Up 1.682691 181.1476 0.9940 HMDB37895
11H-14,15-EETA 0.007826052 Up 1.674163 358.2093 1.2530 C14813
3-O-Benzyl-4,5-O-(1-methylethyldiene)-b-D-fructopyranose 0.022699697 Up 1.646057 310.1397 1.0070
Lupinine 0.015280782 Up 1.632106 169.1493 0.9770 C10773
Perilloside C 0.03297016 Up 1.615675 338.1709 1.1480 HMDB40563
14,15-Epoxy-5,8,11-eicosatrienoic acid Esi + 1.1569998 0.03620125 Up 1.609097 320.2328 1.1570 C14771
Vitamin A 0.03911776 Up 1.604065 286.2295 2.2710 C00473
8,9,10,11-Tetrafluoro-8E,10E-dodecadien-1-ol 0.02570673 Up 1.599283 254.1293 0.9460 LMFA05000168
2,2,11,13,15,16-hexachloro-docosane-1,14-disulfate 0.03122542 Up 1.59914 728.0154 5.2090 LMFA00000019
PG(14:0/16:0) 0.047110956 Up 1.598646 716.4513 3.0500 LMGP04010022
13-L-Hydroperoxylinoleic acid 0.038854554 Up 1.579102 312.2277 1.8080 C04717
9R-(2-cyclopentenyl)-1-nonanol 6.900001 0.03706475 Up 1.574036 232.1829 6.9000 LMFA05000040
Decarbamoylneosaxitoxin 0.039842825 Up 1.572547 272.1243 0.4870 HMDB33663
Rubrobrassicin 0.019814456 Up 1.565009 757.2147 7.0090 LMPK12010026
Isovitexin 2″-O-(6‴-(E)-p-coumaroyl)glucoside 0.021801876 Up 1.5623 762.1733 7.0090 LMPK12110271
Linalyl oxide 0.034410253 Up 1.521542 170.1307 1.7150 HMDB35907
1,8-Diazacyclotetradecane-2,9-dione 0.039644323 Up 1.518688 226.1685 0.4730 C04277
3,4-Dihydrocadalene 0.010371112 Up 1.512875 200.1528 0.4720 HMDB36453
Camptothecin Esi + 1.455 0.036810648 Up 1.457777 370.0917 1.4550 C01897
Imiquimod 0.04839949 Up 1.408694 240.1345 1.1220 HMDB14862
Cycluron 0.039257277 Up 1.328519 220.1547 0.9920 C19109
7″-O-Phosphohygromycin 0.036477257 Up 1.277203 629.1862 2.9640 C03368
Dodecanol 0.04339384 Down −1.38546 208.1831 1.1930 C02277
Aristolochic Acid 0.042970523 Down −1.49828 341.0521 1.1540 C08469
Ceramide (d18:1/22:0) 7.501 0.01739148 Down −1.61369 621.6092 7.5010 C00195
Cer(d18:1/24:1) 0.013293305 Down −1.69156 647.6224 7.4810 C00195
Cer(d18:0/24:1) 0.026877573 Down −1.70931 649.638 7.8760 C00195
2-Hydroxydecanedioic acid 0.003152832 Down −1.9211 240.0977 0.6050 HMDB00424
PE(20:1/20:3) 0.035945572 Down −1.95305 795.5796 6.4140 C00350
N,N,O-Tridesmethyl-tramadol 0.004404348 Down −1.97113 221.1398 0.8020 HMDB60850
Cer(d18:1/23:0) 0.004459221 Down −2.04499 635.6211 7.6940 C00195
Ceramide (d18:1/20:0) 7.063001 0.021412965 Down −2.04623 593.5757 7.0630 C00195
Alpha-CEHC Esi + 0.9440002 0.01759312 Down −2.2515 278.1496 0.9440 HMDB01518
Coenzyme Q9 0.03474693 Down −2.26301 794.6223 8.0660 C01967
Propofol glucuronide 0.028436085 Down −2.39699 354.1736 1.2040 HMDB60933
Colnelenic acid 0.00849326 Down −2.50289 292.2021 1.2090 LMFA10000002
3E,7Z-Tetradecadienyl acetate 0.02242055 Down −2.81641 252.2092 1.2060 LMFA05000348
4-methyl-tridecanedioic acid 0.017504424 Down −2.98563 258.1843 1.0030 LMFA01170017
MG(0:0/18:4/0:0) Esi + 1.455 0.010899141 Down −3.00273 350.2418 1.4550 C01885
MG(0:0/18:4/0:0) 0.015869742 Down −3.90034 350.2434 1.3730 C01885
24R-methylcholest-22E-en-3β,4β,5α,6α,8β,14α,15α,25 R,26-nonol 1.38E-08 Down −25218.3 550.3125 1.3420 LMST01031080
Lipid Negative Metabolites
Compound p(Low vs High) Regulation Fold Change Mass Retention Time Metabolite ID
Seneciphylline 1.58E-07 Up 13797.39 333.156 0.915 C10391
PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885 7.3700004 C00157
PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431 C02737
PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986 C00350
PA(14:0/13:0) 0.018941188 Up 2.03645 614.3692 5.2680006 C00416
PE(20:2/P-18:1) 0.004769958 Up 1.966516 753.5574 7.0680003 C00350
PC(20:3/P-18:0) 0.010843969 Up 1.91783 795.6032 7.6989994 C00157
PE(14:0/22:1) 0.02230334 Up 1.846544 745.5694 5.279 C00350
PS(18:0/20:3) 0.010184665 Up 1.82344 813.5564 5.286 C02737
Ceramide (d18:1/22:0) 0.024829699 Up 1.654491 667.6106 7.5 C00195
Cer(d18:1/24:1) 0.03071489 Up 1.651767 693.6258 7.4820004 C00195
PE(24:0/P-16:0) 0.020011874 Up 1.637358 805.6088 7.485 C00350
PE(O-20:0/22:4) 0.04190944 Up 1.518955 809.6189 7.883001 C13894
PE(22:2/P-18:1) 0.026633823 Up 1.515987 781.5885 7.5039997 C00350
1-(8-[3]-ladderane-octanoyl-2-(8-[3]-ladderane-octanyl)-sn-glycerol 0.039579846 Up 1.211129 650.5179 6.34 LMGL02070009
Ubiquinone-4 0.018471733 Down −1.27887 490.2843 2.4319997 C00399
PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357 5.2099996 C00157
Phytosulfokine b 0.04650307 Down −1.43648 754.1618 1.097 HMDB29810
Rimocidine 0.03588501 Down −1.43716 767.4112 3.0529997 C15821
Acetyl-N-formyl-5-methoxykynurenamine 0.033569902 Down −1.47063 300.0885 1.156 C05642
alpha-Ribazole 0.04385764 Down −1.4749 314.104 1.2270001 C05775
Ceriporic acid A 0.028160162 Down −1.50262 326.2453 1.656 LMFA01170126
PE(14:0/16:0) 0.032299943 Down −1.52035 663.4833 5.243 C00350
CL(18:0/18:1/18:1/18:0) 0.037032653 Down −1.59666 1461.0708 6.4690013 C05980
CL(20:1/18:2/18:1/18:1) 0.024215354 Down −1.66882 1525.0492 5.209 C05980
PC(14:1/P-18:0) 5.355 0.044275247 Down −1.72501 751.5351 5.355 C00157
LysoPE(0:0/22:5) 0.049099866 Down −1.78763 509.2879 1.6539999 C05973
PE(14:1/20:4) 0.009279267 Down −1.84186 709.4657 1.068 C00350
CL(18:0/18:0/18:2/18:0) 0.03732978 Down −2.06192 1457.063 5.2099996 C05980
Camptothecin 0.01822235 Down −2.09969 348.1068 0.9259999 C01897
Aqueous Positive
PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998 C00157
LysoPE(0:0/20:4) 4.39E-08 Up 8191.464 501.2852 1.5950001 C05973
PE(18:2/18:2) 8.68E-08 Up 6742.008 739.5146 2.8740003 C00350
Ceramide(d18:1/17:0) 0.033310328 Up 3.80966 551.5272 0.8509999 C00195
Ceramide(d18:1/17:0) 0.84700006 0.030002557 Up 3.701004 533.5165 0.8470001 C00195
CE(15:0) 0.033051185 Up 2.817563 609.5802 0.856 C02530
Hydroxybutyrylcarnitine 0.049097747 Up 2.040182 247.1433 5.395 HMDB13127
L-Carnitine 0.037892483 Up 1.340674 161.1052 5.8930006 C00318
Hydrocortisone caproate 0.04131846 Down −1.2559 442.272 0.7210001 C13422
1,4′-Bipiperidine-1′-carboxylic acid 0.00321868 Down −1.31586 211.169 1.3240001 C16836
Methylconiine 0.022564428 Down −1.32673 141.1508 1.441 C10159
Acetaminophen glucuronide 3.3980002 0.04299609 Down −1.41856 348.1522 3.3980002 HMDB10316
4-Guanidinobutanoic acid 0.006786303 Down −1.45549 145.085 4.139 C01035
5beta-Gonane 0.008270364 Down −2.18221 254.1995 2.3449998 C19640
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(n = 3 in each experiment). Metabolites were considered significant with a fold change +/− 1.5 and ANOVA p < 0.05 when comparing Ly6Chi and Ly6Clow.

Table 2.

MBROLE functional pathway enrichment of significantly altered metabolites between Ly6Chi and Ly6Clow macrophages.

MBROLE Pathway Enrichment Analysis
KEGG Pathway Glycerophospholipid metabolism p = 0.00000015 Regulation FC Mass Retention Time
Metabolite ID Compound p ([LOW] vs [HI])
HMDB07879 PC(14:0/20:1) 0.00000117 Up 14552.3 759.577 2.7959998
C05973 LysoPE(0:0/20:4) 0.00000004 Up 8191.464 501.2852 1.5950001
C05980 CL(20:4/20:4/18:1/18:1) 0.02237148 Up 2.385773 1501.028 5.2099996
C05980 CL(16:0/18:1/18:1/18:0) 0.02935672 Up 2.216448 1433.044 5.2099996
C02737 PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431
C00416 PA(14:0/13:0) 0.01894119 Up 2.03645 614.3692 5.2680006
C02737 PS(18:0/20:3(8Z,11Z,14Z)) 0.01018467 Up 1.82344 813.5564 5.286
C05980 CL(18:0/18:1/18:1/18:0) 0.03703265 Down −1.59666 1461.071 6.4690013
C05980 CL(20:1/18:2/18:1/18:1) 0.02421535 Down −1.66882 1525.049 5.209
C05973 LysoPE(0:0/22:5) 0.04909987 Down −1.78763 509.2879 1.6539999
C05980 CL(18:0/18:0/18:2/18:0) 0.03732978 Down −2.06192 1457.063 5.2099996
HMDB09093 PE(18:2/18:2) 0.00000009 Down −6742.01 739.5146 2.8740003
HMDB Pathway Arachidonic Acid Metabolism p = 0.025 Regulation FC Mass Retention Time
Metabolite ID Compound p ([LOW] vs [HI])
C00157 PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998
C00157 PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885 7.3700004
C00157 PC(20:3/P-18:0) 0.010843969 Up 1.91783 795.6032 7.6989994
HMDB04693 11H-14,15-EETA 0.007826052 Up 1.674163 358.2093 1.2530001
HMDB04264 14,15-Epoxy-5,8,11-eicosatrienoic acid 0.03620125 Up 1.609097 320.2328 1.1569998
C00157 PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357 5.2099996
C00157 PC(14:1/P-18:0) 5.355 0.044275247 Down −1.72501 751.5351 5.355
HMDB Pathway Phospholipid Biosynthesis p = 0.0000332 Regulation FC Mass Retention Time
Metabolite ID Compound p ([LOW] vs [HI])
C00157 PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998
C00157 PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885 7.3700004
C00350 PE(18:2/18:2) 8.68E-08 Up 6742.008 739.5146 2.8740003
C02737 PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431
C00350 PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986
C00350 PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986
C00416 PA(14:0/13:0) 0.01894119 Up 2.03645 614.3692 5.2680006
C00350 PE(20:2/P-18:1) 0.004769958 Up 1.966516 753.5574 7.0680003
C00157 PC(20:3/P-18:0) 0.010843969 Up 1.91783 795.6032 7.6989994
C00350 PE(14:0/22:1) 0.02230334 Up 1.846544 745.5694 5.279
C02737 PS(18:0/20:3) 0.01018467 Up 1.82344 813.5564 5.286
C00350 PE(24:0/P-16:0) 0.020011874 Up 1.637358 805.6088 7.485
C00350 PE(22:2/P-18:1) 0.026633823 Up 1.515987 781.5885 7.5039997
C00157 PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357 5.2099996
C00350 PE(14:0/16:0) 0.032299943 Down −1.52035 663.4833 5.243
C00157 PC(14:1/P-18:0) 5.355 0.044275247 Down −1.72501 751.5351 5.355
C00350 PE(14:1/20:4) 0.009279267 Down −1.84186 709.4657 1.068
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(n = 3 in each experiment). Pathway enrichment was considered significantly with a MBROLE calculated p < 0.05.

Comparative Proteomic Analysis of Ly6Chi and Ly6Clow Infiltrating Macrophages Following Ethanol Administration

Following methanol extraction of metabolites, the remaining protein pellet was subjected to protein extraction and tryptic digested for mass spectrometry proteomics analysis. Peptides detected by mass spectrometry were searched in Spectrum Mill to determine the protein identification. We detected 1,304 proteins in Ly6Chi and Ly6Clow macrophages with 340 and 214 proteins, respectively, uniquely expressed between macrophage subtypes (Fig. 4A). The 1,304 protein found in the Ly6Chi and Ly6Clow macrophages were subjected to DAVID analysis. From the 1,304 proteins analyzed, we observed 429 biological processes of which 105 were unique for Ly6Clow and 75 for Ly6Chigh macrophages (Fig. 4B). Furthermore, we detected 200 molecular functions from the 1,304 proteins of which 23 are unique for Ly6Clow and 28 for Ly6Chigh macrophages (Fig. 4C). The UniProt accession numbers for the common and unique proteins, biological processes and molecular functions are listed in the Supplementary Information Section (Supplementary Tables S3–S5). Protein quantitative analysis of significantly altered proteins was obtained from Mass Profiler Professional and we detected 47 differentially regulated proteins between the Ly6Chi and Ly6Clow macrophages (Table 3). The significantly altered proteins between the Ly6Chi and Ly6Clow macrophages were analyzed using the DAVID bioinformatics resource and we observed a total of 21 biological processes and 9 molecular functions from DAVID analysis of the protein quantification obtained (Supplementary Table S2). Of the significantly enriched biological processes and molecular functions, immune processes, actin polymerization and organization, phagocytosis, apoptotic processes and antigen presentation were selected for additional literature based analysis in their potential role for regulating macrophage polarization (Table 4).

Figure 4.

Figure 4

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Venn Diagrams of unique protein, biological processes and molecular functions between Ly6Chi and Ly6Clow macrophages. (A) Number of common and unique proteins between Ly6Chi and Ly6Clow macrophages. (B) Number of common and unique biological processes between Ly6Chi and Ly6Clow macrophages. (C) Number of common and unique molecular functions between Ly6Chi and Ly6Clow macrophages. Lists of common and unique protein, biological processes and molecular functions can be found in Supplementary Tables S3–S5.

Table 3.

Quantitative analysis of MS-only spectra of significantly altered proteins between Ly6Chi and Ly6Clow macrophages.

Quantitative Proteomics Analysis
Protein Name Protein ID Peptide # p-value Fold Change (Low vs High) Regulation
Phospholipase D3 O35405 2 8.24E-09 32371.51 Up
Cathepsin L1 P06797 4 2.77E-08 5875.09 Up
Ras-related protein Rap-1b Q99JI6 2 4.55E-02 1512.80 Up
Protein S100-A9 P31725 8 4.06E-05 32.22 Up
Protein S100-A8 P27005 5 2.17E-04 31.14 Up
Cathelin-related antimicrobial peptide P51437 2 1.72E-05 28.13 Up
H-2 class II histocompatibility antigen, A-B alpha chain P14434 4 4.57E-04 17.26 Up
H-2 class II histocompatibility antigen, A beta chain P14483 4 1.85E-04 15.85 Up
Lactotransferrin P08071 14 4.63E-04 14.67 Up
Neutrophil gelatinase-associated lipocalin P11672 2 2.42E-03 14.47 Up
Macrophage asialoglycoprotein-binding protein 1 P49300 4 3.92E-04 6.48 Up
H-2 class II histocompatibility antigen gamma chain P04441 4 7.13E-04 4.74 Up
CD177 antigen Q8R2S8 3 8.01E-04 4.70 Up
Gelsolin P13020 11 1.36E-03 3.17 Up
Transcription factor A, mitochondrial P40630 2 9.01E-03 3.09 Up
Vasodilator-stimulated phosphoprotein P70460 2 1.67E-02 2.91 Up
EF-hand domain-containing protein D2 Q9D8Y0 3 2.42E-02 2.79 Up
Putative phospholipase B-like 1 Q8VCI0 6 9.23E-03 2.54 Up
Chitinase-3-like protein 3 O35744 13 6.29E-03 2.54 Up
Synaptosomal-associated protein 23 O09044 2 4.40E-03 2.51 Up
Low affinity immunoglobulin gamma Fc region receptor II P08101 4 1.73E-02 2.46 Up
Histone H1.3 P43277 2 1.11E-02 2.33 Up
Lymphocyte-specific protein 1 P19973 10 2.03E-02 2.15 Up
C-type lectin domain family 4 member F P70194 12 5.30E-03 2.12 Up
Allograft inflammatory factor 1 O70200 3 5.35E-03 2.11 Up
Alpha-actinin-1 Q7TPR4 20 5.04E-03 2.08 Up
Hematopoietic lineage cell-specific protein P49710 10 1.66E-02 2.02 Up
EF-hand domain-containing protein D1 Q9D4J1 3 2.92E-02 2.02 Up
Tyrosine-protein phosphatase non-receptor type substrate 1 P97797 2 4.56E-02 1.97 Up
Histone H1.0 P10922 2 1.22E-02 1.87 Up
Annexin A1 P10107 14 1.22E-02 1.86 Up
Integrin alpha-L P24063 4 1.58E-02 1.74 Up
Prelamin-A/C P48678 9 2.11E-02 1.52 Up
ATP synthase subunit alpha, mitochondrial Q03265 16 3.66E-02 1.49 Up
ATP synthase subunit beta, mitochondrial P56480 25 4.22E-02 1.43 Up
Lysosome-associated membrane glycoprotein 1 P11438 3 4.39E-02 −1.45 Down
Filamin-A Q8BTM8 59 2.53E-02 −1.57 Down
V-type proton ATPase subunit B, brain isoform P62814 3 2.16E-02 −1.79 Down
Plectin Q9QXS1 4 1.65E-02 −1.80 Down
Proliferation-associated protein 2G4 P50580 4 1.91E-02 −1.85 Down
DNA-binding protein A Q9JKB3 2 3.33E-02 −1.88 Down
Glutathione S-transferase Mu 1 P10649 8 2.57E-02 −1.90 Down
Tubulin alpha-4A chain P68368 3 1.92E-02 −1.98 Down
Polyadenylate-binding protein 1 P29341 5 6.88E-03 −2.01 Down
Isocitrate dehydrogenase [NADP] cytoplasmic O88844 4 4.32E-02 −2.18 Down
Lysozyme C-1 P17897 2 8.32E-03 −2.27 Down
Coagulation factor XIII A chain Q8BH61 13 6.73E-03 −2.99 Down
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(n = 3 in each experiment). Protein were considered significant with a Mass Protein Profiler calculated ANOVA p < 0.05 when comparing Ly6Chi and Ly6Clow.

Table 4.

DAVID functional pathway enrichment of significantly altered proteins between Ly6Chi and Ly6Clow macrophages.

Biological Processes Low vs High
GO ID Term Count % PValue Fold Enrichment
GO:0019886 Antigen Processing and Presentation of Exogenous Peptide Antigen via MHC Class II 4 8.51 0.00001 112.31
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4 0.00046 17.26 Up
P14483 H-2 class II histocompatibility antigen, A beta chain 4 0.00019 15.85 Up
P04441 H-2 class II histocompatibility antigen gamma chain 4 0.00071 4.74 Up
P08101 Low affinity immunoglobulin gamma Fc region receptor II 4 0.01726 2.46 Up
GO ID Term Count % PValue Fold Enrichment
GO:0019882 Antigen Processing and Presentation 3 6.38 0.00798 21.84
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4 0.00046 17.26 Up
P14483 H-2 class II histocompatibility antigen, A beta chain 4 0.00019 15.85 Up
P04441 H-2 class II histocompatibility antigen gamma chain 4 0.00071 4.74 Up
GO ID Term Count % PValue Fold Enrichment
GO:0030041 Actin Filament Polymerization 3 6.38 0.00161 49.14
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P13020 Gelsolin 11 0.00136 3.17 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
P49710 Hematopoietic lineage cell-specific protein 10 0.01658 2.02 Up
GO ID Term Count % PValue Fold Enrichment
GO:0031532 Actin Cytoskeleton Reorganization 3 6.38 0.00742 22.68
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P10107 Annexin A1 14 0.01223 1.86 Up
Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
GO ID Term Count % PValue Fold Enrichment
GO:0006911 Phagocytosis, Engulfment 4 8.51 0.00019 34.94
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P13020 Gelsolin 11 0.00136 3.17 Up
P08101 Low affinity immunoglobulin gamma Fc region receptor II 4 0.01726 2.46 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
P97797 Tyrosine-protein phosphatase non-receptor type substrate 1 2 0.04559 1.97 Up
GO ID Term Count % PValue Fold Enrichment
GO:0002376 Immune System Process 8 17.02 0.00004 8.21
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P27005 Protein S100-A8 5 0.00022 31.14 Up
P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4 0.00046 17.26 Up
P14483 H-2 class II histocompatibility antigen, A beta chain 4 0.00019 15.85 Up
P08071 Lactotransferrin 14 0.00046 14.67 Up
P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47 Up
P04441 H-2 class II histocompatibility antigen gamma chain 4 0.00071 4.74 Up
P10107 Annexin A1 14 0.01223 1.86 Up
GO ID Term Count % PValue Fold Enrichment
GO:0006955 Immune Response 4 8.51 0.03003 5.78
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4 0.00046 17.26 Up
P14483 H-2 class II histocompatibility antigen, A beta chain 4 0.00019 15.85 Up
P04441 H-2 class II histocompatibility antigen gamma chain 4 0.00071 4.74 Up
P08101 Low affinity immunoglobulin gamma Fc region receptor II 4 0.01726 2.46 Up
GO ID Term Count % PValue Fold Enrichment
GO:0006954 Inflammatory Response 5 10.64 0.01038 5.71
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P27005 Protein S100-A8 5 0.00022 31.14 Up
O35744 Chitinase-3-like protein 3 13 0.00629 2.54 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
P10107 Annexin A1 14 0.01223 1.86 Up
GO ID Term Count % PValue Fold Enrichment
GO:0045087 Innate Immune Response 5 10.64 0.01722 4.91
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P27005 Protein S100-A8 5 0.00022 31.14 Up
P51437 Cathelin-related antimicrobial peptide 2 0.00002 28.13 Up
P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47 Up
P10107 Annexin A1 14 0.01223 1.86 Up
GO ID Term Count % PValue Fold Enrichment
GO:0043066 Negative Regulation of Apoptotic process 6 12.77 0.01286 4.17
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P08071 Lactotransferrin 14 0.00046 14.67 Up
P04441 H-2 class II histocompatibility antigen gamma chain 4 0.00071 4.74 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
P50580 Proliferation-associated protein 2G4 4 0.01913 −1.85 Down
Q9JKB3 DNA-binding protein A 2 0.03331 −1.88 Down
GO ID Term Count % PValue Fold Enrichment
GO:0006915 Apoptotic Process 5 10.64 0.05269 3.45
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P27005 Protein S100-A8 5 0.00022 31.14 Up
P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47 Up
P13020 Gelsolin 11 0.00136 3.17 Up
P19973 Lymphocyte-specific protein 1 10 0.02033 2.15 Up
Molecular Functions Low vs High
GO ID Term Count % PValue Fold Enrichment
GO:0003779 Actin Binding 8 17.02 0.00002 9.18
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P13020 Gelsolin 11 0.00136 3.17 Up
P70460 Vasodilator-stimulated phosphoprotein 2 0.01674 2.91 Up
P19973 Lymphocyte-specific protein 1 10 0.02033 2.15 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
P49710 Hematopoietic lineage cell-specific protein 10 0.01658 2.02 Up
Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
Q9QXS1 Plectin 4 0.01647 −1.80 Down
GO ID Term Count % PValue Fold Enrichment
GO:0051015 Actin Filament Binding 3 6.38 0.04371 8.81
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
GO ID Term Count % PValue Fold Enrichment
GO:0005509 Calcium Ion Binding 9 19.15 0.00031 4.99
Protein ID Protein Name Peptide # p(Low vs High) Fold Change Regulation
P31725 Protein S100-A9 8 0.00004 32.22 Up
P27005 Protein S100-A8 5 0.00022 31.14 Up
P13020 Gelsolin 11 0.00136 3.17 Up
Q9D8Y0 EF-hand domain-containing protein D2 3 0.02416 2.79 Up
O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
Q9D4J1 EF-hand domain-containing protein D1 3 0.02919 2.02 Up
P10107 Annexin A1 14 0.01223 1.86 Up
P56480 ATP synthase subunit beta, mitochondrial 25 0.04219 1.43 Up
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(n = 3 in each experiment). Pathway enrichment was considered significant with a DAVID calculated t-test p < 0.05 when comparing Ly6Chi and Ly6Clow.

Discussion

Alcoholic liver disease is a major public health issue and accounts for approximately 48% of liver cirrhosis related deaths1. As infiltrating macrophages are known to mediate the pathogenesis of ALD from steatosis to cirrhosis8–10, analysis of the altered signaling pathways between the different subsets of these cells in response to ethanol is of the utmost importance in developing treatment options to prevent the progression of ALD or promote the reversal of scar tissue formation in the liver. Macrophages display a remarkable capacity to adapt their phenotype based on tissue micro-environmental cues such as lipid exposure, hypoxia, cytokines, and efferocytosis of apoptotic cells21,25. To date, no studies have been conducted providing analysis of the cellular metabolome and proteome of infiltrating liver macrophages isolated from an in vivo model of ALD. While several studies have utilized immortalized mouse macrophages (RAW264.7) for transcriptomic26 and lipidomic26–28 analysis following inflammatory stimuli, this study is the first to look at in vivo polarized macrophages in an ALD model, therefore allowing for the natural effects of the tissue microenvironment, such as the gut-liver signaling axis, and ethanol metabolism on regulating liver infiltrating macrophage phenotypes.

It has previously been shown that following phagocytosis of apoptotic hepatocytes, Ly6Chi macrophages differentiate into Ly6Clow macrophages which express higher levels of phagocytosis related genes after alcohol intake21. In healthy or control diet fed mouse livers, infiltrating macrophages are limited until liver insult elicits the recruitment of Ly6C+ monocytes into the liver tissue.11,21,25 Therefore, the analysis done in this study was focused on the difference between Ly6Chi and Ly6Clow macrophages from ethanol fed mice without comparison to control diet fed animals. In our present study, we observed a significant increase in phagocytosis and engulfment related proteins (Table 4). We detected an upregulation of phagocytosis related proteins in Ly6Clow macrophages; this is expected as phagocytosis of apoptotic cells induces an anti-inflammatory phenotype29,30. Additionally, we saw a significant enrichment in proteins involved in regulating the apoptotic process. Furthermore, we observed a significant enrichment in actin polymerization and cytoskeletal reorganization in Ly6Clow macrophages. Alterations in actin contractility, cytoskeletal organization and cellular elongation have been shown to induce macrophages to an anti-inflammatory phenotype as evidenced by increased arginase-1 and YM-1 expression, hallmarks of anti-inflammatory macrophage polarization31. Additionally, defects in actin polymerization have been shown to attenuate macrophage phagocytic ability32. This suggests further in vivo analysis of actin polymerization and cytoskeletal organization in murine macrophages may elucidate a novel therapeutic strategy in modulating macrophage phenotypes in ALD by affecting macrophage phagocytosis and response to apoptotic stimuli.

Recently Zhang et al. provided a comprehensive analysis profiling lipid species during in vitro differentiation of mouse and human macrophages cell lines. They reported a significant increase in the composition of glycerophospholipid species during macrophage differentiation. Furthermore, they saw a significant increase in the levels of lysophospholipids in anti-inflammatory macrophages compared to pro-inflammatory macrophages suggesting that modulation of glycerophospholipid metabolism could be a vital signaling component in differentiation of liver macrophage phenotypes33. In our study, we found a significant enrichment in glycerophospholpid metabolism with differential metabolite regulation between Ly6Chi and Ly6Clow macrophages. Additionally, we observed enrichment for arachidonic acid metabolism and phospholipid biosynthesis (Table 2). In each of the enriched pathways, we detected a massive upregulation in multiple phosphatidylcholine (PC) species in Ly6Clow macrophages. PCs has been shown to promote an anti-inflammatory phenotype in macrophages through modulating actin assembly and increasing mycobacterium growth in RAW264.7 and J774 macrophages34. Likewise, we observed a substantial upregulation in phosphatidylethanolamine (PE(18:2/18:2) in Ly6Clow macrophages. Following stimulation with nonsteroidal anti-inflammatory agents, macrophages have been shown to display an increase in multiple PE species and take on an anti-inflammatory phenotype35. Therefore, the observed changes we see in PC and PE species correlate with in vitro studies highlighting the anti-inflammatory properties of PC and PE glycerophospholipid species in modulating macrophage phenotypes. Also of interest in regard to PE(18:2/18:2) is the linonleic acid (18:2) constituents present at the sn-1 and sn-2 positions, as linoleic acid has been shown to promote an anti-inflammatory phenotype in macrophages36. These results suggest the involvement of phospholipase A2 (PLA2) in regulating macrophage polarization in ALD. PLA2 is involved in the hydrolysis of sn-2 fatty acids from membrane glycerophospholipids yielding a free fatty acid, arachidonic acid, and a lysophospholipid37. The functions of PLA2 in modulating the inflammatory response have been well established in a variety of inflammatory contexts38–42. Ishihara et al. have shown that targeting cytosolic PLA2 activity in non-alcoholic fatty liver disease models proved beneficial in preventing hepatic fibrosis formation and reducing hepatocyte death43,44. Rodrigues et al. showed that using diethylcarbmazine, which modulates arachidonic acid metabolism and cyclooxygenase-2 (COX-2) mediated prostaglandin production, elicited an anti-inflammatory and protective response in ALD45. In addition to COX-2 mediated arachidonic acid metabolism and prostaglandin synthesis, arachidonic acid can be metabolized via cytochrome P450 epoxygenase mediated pathway to generate epoxyeicosatrienoic acids (EETs)46. We found a significant increase in EETs in the Ly6Clow phenotype. Endogenous EETs have been shown to regulate the ability of in vitro THP-1 monocytes to differentiate into pro-inflammatory macrophages in response to pro-inflammatory stimuli (lipopolysaccharide (LPS) and interferon γ (IFNγ) as well as preventing differentiation under anti-inflammatory stimuli (IL-4)46. Additionally, it has been shown that the immunomodulatory effect of EETs on inducing pro-inflammatory macrophage differentiation was facilitated through attenuation of NF-κB signaling47. Finally, studies have shown that eicosatrienoic acid inhibits LPS induced inflammatory gene expression in macrophages48. We detected an upregulation of eicosatrienoic acid metabolites in the anti-inflammatory, Ly6Clow macrophages after alcohol consumption. These studies coupled with the observed increase in arachidonic acid, glycerophospolipid metabolism and phospholipid biosynthesis as well as increased calcium ion binding suggest future investigation of the role of calcium dependent and independent PLA2 activity for therapeutic targeting of macrophage polarization in ALD.

The present study provides a framework for future studies utilizing multi-omics approaches for analyzing signaling difference between pro- and anti-inflammatory macrophages isolated from ALD mouse models. We detected a number of metabolic and protein mediated pathways that were significantly altered between the two macrophage subtypes, validating a number of in vitro studies analyzing the lipid, metabolite, and protein profile of polarized macrophages26–28,33,48. While the present study utilized an ALD model in which the degree of inflammation is not as evident histopathologically as more aggressive models, such as the NIAAA model, it allowed for the sufficient isolation of infiltrating liver macrophages not normally present in the healthy liver. We identified a number of metabolic pathways significantly altered due to the early onset of alcohol-induced hepatic inflammation (arachidonic acid metabolism, glycerophospholipid metabolism and phospholipid biosynthesis), which suggests that PLA2 enzymes play a critical role in modulating macrophage phenotypes. To explore the impact of PLA2 on ALD, future studies could utilize whole body PLA2 knockout mice or known PLA2 pharmacological inhibitors to elucidate the impact of PLA2 on macrophage polarization in ALD models. Overall, the data presented here justifies a further need to investigate numerous signaling mechanisms implicated in the modulation of macrophage phenotypes during ALD.

Materials and Methods

Animal Model

Female C57BL/6 J mice (The Jackson Laboratory, Bar Harbor, ME, USA) (n = 30) were maintained under pathogen-free conditions in the Center for Laboratory Animal Care at the University of Colorado Anschutz Medical Campus (Aurora, CO, USA). All experiments were performed using an Institutional Animal Care and Use Committee (IACUC) approved protocol and in accordance to the guidelines of the IACUC at the University of Colorado Anschutz Medical Campus. To elicit infiltrating macrophage recruitment to the liver, mice were fed an ethanol-containing Lieber-Decarli liquid diet (Bio-Serv, Flemington, NJ, USA). Ethanol content was introduced gradually by increasing 1.6% (v/v) every 2 days until 5%. All mice were then fed the liquid diet containing 5% ethanol for 4 weeks, as described previously49,50.

Isolation of Liver Non-Parenchymal Cells (NPCs)

Liver NPCs were isolated following a previously described method51. Briefly, a 20-G catheter was put through the mouse superior vena cava, the inferior vena cava was clamped, and the portal vein cut. The liver was perfused with Hank’s balanced salt solution (HBSS), followed by a digestion buffer [1 × HBSS, supplemented with 0.04% collagenase (type IV; Sigma, St. Louis, MO, USA), 1.25 mM CaCl2, 4 mM MgSO4, and 10 mM HEPES]. After digestion, the liver was disrupted in ACD solution (1 × HBSS, supplemented with 0.5% FBS, 0.6% citrate-dextrose solution, and 10 mM HEPES). Single cells were passed through a 100-μm cell strainer, and the cells were fractionated using 30% (w/v) Nycodenz (Axis-Shield PoC AS, Oslo, Norway) at 1.155 g/mL to yield liver NPCs and further purified using 30% Percoll (Sigma) at 1.04 g/mL.

Flow Cytometry Assisted Cell Sorting (FACS)

To purify KCs, Ly6Chi and Ly6Clow IMs, liver NPCs were incubated with normal rat serum (Sigma) and anti-mouse FcγRII/III (Becton Dickinson, Franklin Lakes, NJ, USA) to minimize nonspecific antibody binding. Subsequently, the cells were stained with anti-CD45, anti-Ly6C, anti-Ly6G, anti-CD19, anti-SiglecF (Becton Dickinson) and anti-F4/80, anti-CD11b, anti-NK1.1 and anti-CD3 (eBioscience, San Diego, CA, USA), and sorted using a BD FACSAria II Cell Sorter (BD Bioscience, San Jose, CA, USA).

Metabolomics Sample Preparation and Analysis

Cell pellets from different sort dates were combined in order to get 3 technical replicates of approximately 400–500 K cells per sample type (Ly6Chi and Ly6Clow). Extractions were performed using volumes of 70% MeOH/water and 100% MeOH based on cell numbers. Cold methanol was used to precipitate proteins prior to liquid-liquid extraction of metabolites. Proteins pellets were saved for future proteomics analysis. Liquid-liquid extraction was performed on the supernatant using water and methyl tert-butyl ether (MTBE).The aqueous and lipid fractions were retained for analysis. Lipid fractions were analyzed using SB-C18 HPLC analytical column in positive and negative ionization mode on the Agilent 6560 IM-QTOF (in QTOF mode only). Aqueous fractions were analyzed using a HILIC column in positive ionization mode on the Agilent 6560 IM-QTOF (in QTOF mode only). A pooled sample was used as instrument QCs to monitor the entire instrument analysis. Initial data QC, peak threshold evaluation, retention time variation, and charge carrier evaluation was performed in Agilent MassHunter Qualitative Analysis, version B.07.00. Data extraction was performed in MassHunter Profinder, version B.08.00. Differential Analysis was performed in Agilent Mass Profiler Professional (MPP), version 14.5. Compound annotation (database searches and molecular formula generation) was performed in MassHunter ID Browser software, version 14.5. Raw MS data were checked for quality and reproducibility. Appropriate spectral and chromatogram peak height thresholds were determined by careful examination of the raw data. Appropriate charge carriers to be allowed during data extraction were determined after preliminary extraction on selected samples. The Agilent “recursive workflow” was used to prepare data. This workflow includes the following steps: 1) untargeted extraction using the Find-by-Molecular Feature algorithm, 2) mass and time alignment of extracted compounds, 3) targeted extraction using the Find-by-Ion algorithm (using the list of ions prepared in step 1), 4) final mass and time alignment of extracted compounds.

Metaboanalyst and Metabolites Biological Role (MBROLE) Analysis

For Metaboanalyst comparison the following analysis parameters were used: Mass Tolerance: 0.05, No Missing Value Imputation, Data Filtering: Mean Intensity Value, Sample Normalization: Normalization by Sum, Data Transformation: None, Data Scaling: Mean Centering, Fold Change Threshold: 2, T-test: Group Variance Equal. For MBROLE metabolite functional enrichment analysis, pathways were considered significant with a p < 0.05.

Proteomics Sample Preparation, nHPLC-MS and nHPLC-MS/MS Analysis

Following methanol extraction for metabolomics, the remaining cell pellets from each technical replicate were processed using the PreOmics iST 8x Kit (Cat # 00001) following the included protocol. Digested macrophage samples were loaded onto a 2 cm PepMAP 100, nanoviper trapping column and chromatographically resolved on-line using a 0.075 × 250 mm, 2.0 µm Acclaim PepMap RSLC reverse phase nano column (Thermo Scientific) using a 1290 Infinity II LC system equipped with a nanoadapter (Agilent). Mobile phases consisted of water + 0.1% formic acid (A) and 90% aq. acetonitrile + 0.1% formic acid (B). Samples were loaded onto the trapping column at 3.2 μL/min for 2.5 minutes at initial condition before being chromatographically separated at an effective flow rate of 345 nl/min using a gradient of 3–8.5% B over 4.0 minutes, 8.5–26% B over 48.5 minutes, and 26–35% over 7.5 minutes for a total 60 minute gradient. The gradient method was followed by a column wash at 70% B for 5 minutes. For nHPLC-MS, data was collected with a 6550 QTOF equipped with a nano source (Agilent) operated in MS mode. For nHPLC-MS/MS, data was collected with a 6550 QTOF equipped with a nano source (Agilent) operated using Data Dependent Acquisition CID Auto MS/MS. The capillary voltage, drying gas flow, and drying gas temperature were set to 1300 V, 11.0 l/min, and 200 C, respectively. Data was collected in positive ion polarity over mass ranges 290–1700 m/z at a scan rate of 1.5 spectra/s. MS/MS scans were collected over mass ranges 50–1700 m/z at a scan rate of 3 spectra/s. Singly charged species were excluded from being selected during MS/MS acquisition. Following data acquisition in MS/MS mode, sample data was searched in SpectrumMill to identify proteins.

DAVID Bioinformatics Analysis

Functional pathway enrichment of significantly altered proteins between Ly6Chi and Ly6Clow macrophage population was analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resource 6.8. For pathway enrichment, significantly altered proteins were compared to the whole mouse background. Pathways were considered significant with at least 3 proteins involved, a fold enrichment >2, and a p < 0.05.

Statistical Analysis

Statistical analysis of significantly altered metabolites and proteins was determined using Mass Profiler Professional Software. For Metaboanalyst, significantly altered metabolites were determined based of the difference in peak height intensity between the analyze metabolites with a p < 0.05. For MBROLE analysis for metabolite functional pathway enrichment, pathways were considered significant with a p < 0.05. For DAVID pathway enrichment, significantly altered proteins were compared to the whole mouse background. Pathways were considered significant with at least 3 proteins involved, a fold enrichment >2, and a p < 0.05.

Supplementary information

Supplementary Data (1.1MB, pdf)

Acknowledgements

We thank the Mass Spectrometry Core in the Skaggs School of Pharmacy for their assistance with proteomics and metabolomics. Supported by the National Institute of Alcohol Abuse and Alcoholism (NIAAA) (Grants U01AA021723, R21AA024636 and R01DK10957 to C.J.), NIAAA (Grant R01AA022146 to K.S.F.).

Author Contributions

J.O.M. performed data analysis, data interpretation and drafted the manuscript. M.W. performed animal experiments and isolated macrophages. C.R.M. and R.P. performed proteomic sample preparation and analysis. X.Z. performed metabolomics sample preparation and data analysis. N.R. performed data analysis and interpretation. K.S.F. assisted in data interpretation and funded the proteomic analysis. C.J. conceived the study and funded animal experiments and metabolomics analysis. The authors read and approved the final manuscript.

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

John O. Marentette and Meng Wang contributed equally.

Contributor Information

Kristofer S. Fritz, Email: Kristofer.Fritz@ucdenver.edu

Cynthia Ju, Email: Changqing.Ju@uth.tmc.edu.

Supplementary information

Supplementary information accompanies this paper at 10.1038/s41598-019-43240-4.

References

  • 1.Gao B, Bataller R. Alcoholic liver disease: pathogenesis and new therapeutic targets. Gastroenterology. 2011;141:1572–1585. doi: 10.1053/j.gastro.2011.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rubio A, et al. Identification of a gene-pathway associated with non-alcoholic steatohepatitis. J Hepatol. 2007;46:708–718. doi: 10.1016/j.jhep.2006.10.021. [DOI] [PubMed] [Google Scholar]
  • 3.Baraona E, Lieber CS. Effects of ethanol on lipid metabolism. J Lipid Res. 1979;20:289–315. [PubMed] [Google Scholar]
  • 4.Feldstein AE, et al. Hepatocyte apoptosis and fas expression are prominent features of human nonalcoholic steatohepatitis. Gastroenterology. 2003;125:437–443. doi: 10.1016/S0016-5085(03)00907-7. [DOI] [PubMed] [Google Scholar]
  • 5.Wieckowska A, et al. In vivo assessment of liver cell apoptosis as a novel biomarker of disease severity in nonalcoholic fatty liver disease. Hepatology. 2006;44:27–33. doi: 10.1002/hep.21223. [DOI] [PubMed] [Google Scholar]
  • 6.Marra F, Gastaldelli A, Svegliati Baroni G, Tell G, Tiribelli C. Molecular basis and mechanisms of progression of non-alcoholic steatohepatitis. Trends Mol Med. 2008;14:72–81. doi: 10.1016/j.molmed.2007.12.003. [DOI] [PubMed] [Google Scholar]
  • 7.Capece D, et al. The inflammatory microenvironment in hepatocellular carcinoma: a pivotal role for tumor-associated macrophages. Biomed Res Int. 2013;2013:187204. doi: 10.1155/2013/187204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tapia-Abellan A, et al. The peritoneal macrophage inflammatory profile in cirrhosis depends on the alcoholic or hepatitis C viral etiology and is related to ERK phosphorylation. BMC Immunol. 2012;13:42. doi: 10.1186/1471-2172-13-42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Petrasek J, et al. IL-1 receptor antagonist ameliorates inflammasome-dependent alcoholic steatohepatitis in mice. J Clin Invest. 2012;122:3476–3489. doi: 10.1172/JCI60777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Enomoto N, et al. Role of Kupffer cells and gut-derived endotoxins in alcoholic liver injury. J Gastroenterol Hepatol. 2000;15(Suppl):D20–25. doi: 10.1046/j.1440-1746.2000.02179.x. [DOI] [PubMed] [Google Scholar]
  • 11.Ju C, Tacke F. Hepatic macrophages in homeostasis and liver diseases: from pathogenesis to novel therapeutic strategies. Cell Mol Immunol. 2016;13:316–327. doi: 10.1038/cmi.2015.104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Sica A, Erreni M, Allavena P, Porta C. Macrophage polarization in pathology. Cell Mol Life Sci. 2015;72:4111–4126. doi: 10.1007/s00018-015-1995-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Serbina NV, Shi C, Pamer EG. Monocyte-mediated immune defense against murine Listeria monocytogenes infection. Adv Immunol. 2012;113:119–134. doi: 10.1016/B978-0-12-394590-7.00003-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Karlmark KR, et al. Hepatic recruitment of the inflammatory Gr1+ monocyte subset upon liver injury promotes hepatic fibrosis. Hepatology. 2009;50:261–274. doi: 10.1002/hep.22950. [DOI] [PubMed] [Google Scholar]
  • 15.Tacke F, Randolph GJ. Migratory fate and differentiation of blood monocyte subsets. Immunobiology. 2006;211:609–618. doi: 10.1016/j.imbio.2006.05.025. [DOI] [PubMed] [Google Scholar]
  • 16.Kolb M, Margetts PJ, Anthony DC, Pitossi F, Gauldie J. Transient expression of IL-1beta induces acute lung injury and chronic repair leading to pulmonary fibrosis. J Clin Invest. 2001;107:1529–1536. doi: 10.1172/JCI12568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kinnman N, et al. PDGF-mediated chemoattraction of hepatic stellate cells by bile duct segments in cholestatic liver injury. Lab Invest. 2000;80:697–707. doi: 10.1038/labinvest.3780073. [DOI] [PubMed] [Google Scholar]
  • 18.Seki E, et al. TLR4 enhances TGF-beta signaling and hepatic fibrosis. Nat Med. 2007;13:1324–1332. doi: 10.1038/nm1663. [DOI] [PubMed] [Google Scholar]
  • 19.Paradis V, et al. Effects and regulation of connective tissue growth factor on hepatic stellate cells. Laboratory Investigation. 2002;82:767–773. doi: 10.1097/01.Lab.0000017365.18894.D3. [DOI] [PubMed] [Google Scholar]
  • 20.Dal-Secco D, et al. A dynamic spectrum of monocytes arising from the in situ reprogramming of CCR2+ monocytes at a site of sterile injury. J Exp Med. 2015;212:447–456. doi: 10.1084/jem.20141539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wang M, et al. Chronic alcohol ingestion modulates hepatic macrophage populations and functions in mice. J Leukoc Biol. 2014;96:657–665. doi: 10.1189/jlb.6A0114-004RR. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Lumeng CN, Bodzin JL, Saltiel AR. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J Clin Invest. 2007;117:175–184. doi: 10.1172/JCI29881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wynn TA. IL-13 effector functions. Annu Rev Immunol. 2003;21:425–456. doi: 10.1146/annurev.immunol.21.120601.141142. [DOI] [PubMed] [Google Scholar]
  • 24.Movahedi K, et al. Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6C(high) monocytes. Cancer Res. 2010;70:5728–5739. doi: 10.1158/0008-5472.CAN-09-4672. [DOI] [PubMed] [Google Scholar]
  • 25.Marentette, J. & Ju, C. In Molecules, Systems and Signaling in Liver Injury Cell Death in Biology and Diseases (eds W. X. Ding & X. M. Yin) 53–74 (Springer, Cham, 2017).
  • 26.Maurya MR, et al. Analysis of inflammatory and lipid metabolic networks across RAW264.7 and thioglycolate-elicited macrophages. J Lipid Res. 2013;54:2525–2542. doi: 10.1194/jlr.M040212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Andreyev AY, et al. Subcellular organelle lipidomics in TLR-4-activated macrophages. J Lipid Res. 2010;51:2785–2797. doi: 10.1194/jlr.M008748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Dennis EA, et al. A mouse macrophage lipidome. J Biol Chem. 2010;285:39976–39985. doi: 10.1074/jbc.M110.182915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Huynh ML, Fadok VA, Henson PM. Phosphatidylserine-dependent ingestion of apoptotic cells promotes TGF-beta1 secretion and the resolution of inflammation. J Clin Invest. 2002;109:41–50. doi: 10.1172/JCI11638. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Pupjalis D, Goetsch J, Kottas DJ, Gerke V, Rescher U. Annexin A1 released from apoptotic cells acts through formyl peptide receptors to dampen inflammatory monocyte activation via JAK/STAT/SOCS signalling. EMBO Mol Med. 2011;3:102–114. doi: 10.1002/emmm.201000113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.McWhorter FY, Wang T, Nguyen P, Chung T, Liu WF. Modulation of macrophage phenotype by cell shape. Proc Natl Acad Sci USA. 2013;110:17253–17258. doi: 10.1073/pnas.1308887110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ninkovic J, Roy S. Morphine decreases bacterial phagocytosis by inhibiting actin polymerization through cAMP-, Rac-1-, and p38 MAPK-dependent mechanisms. Am J Pathol. 2012;180:1068–1079. doi: 10.1016/j.ajpath.2011.11.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zhang C, et al. Quantitative profiling of glycerophospholipids during mouse and human macrophage differentiation using targeted mass spectrometry. Sci Rep. 2017;7:412. doi: 10.1038/s41598-017-00341-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Treede I, et al. Anti-inflammatory effects of phosphatidylcholine. J Biol Chem. 2007;282:27155–27164. doi: 10.1074/jbc.M704408200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zhang, G., Zhao, L., Zhu, J., Feng, Y. & Wu, X. Anti-inflammatory activities and glycerophospholipids metabolism in KLA-stimulated RAW 264.7 macrophage cells by diarylheptanoids from the rhizomes of Alpinia officinarum. Biomed Chromatogr32, 10.1002/bmc.4094 (2018). [DOI] [PubMed]
  • 36.de Gaetano M, Alghamdi K, Marcone S, Belton O. Conjugated linoleic acid induces an atheroprotective macrophage MPhi2 phenotype and limits foam cell formation. J Inflamm (Lond) 2015;12:15. doi: 10.1186/s12950-015-0060-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rastogi P, McHowat J. Inhibition of calcium-independent phospholipase A2 prevents inflammatory mediator production in pulmonary microvascular endothelium. Respir Physiol Neurobiol. 2009;165:167–174. doi: 10.1016/j.resp.2008.11.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Marentette, J., Kolar, G. & McHowat, J. Increased susceptibility to bladder inflammation in smokers: targeting the PAF-PAF receptor interaction to manage inflammatory cell recruitment. Physiol Rep3, 10.14814/phy2.12641 (2015). [DOI] [PMC free article] [PubMed]
  • 39.Marentette, J. & McHowat, J. Recruitment of inflammatory cells to the bladder endothelium exposed to cigarette smoke extract. Faseb J28 (2014).
  • 40.Sharma J, et al. Lung endothelial cell platelet-activating factor production and inflammatory cell adherence are increased in response to cigarette smoke component exposure. Am J Physiol-Lung C. 2012;302:L47–L55. doi: 10.1152/ajplung.00179.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Kispert, S. E. et al. Cigarette smoke-induced urothelial cell damage: potential role of platelet-activating factor. Physiol Rep5, 10.14814/phy2.13177 (2017). [DOI] [PMC free article] [PubMed]
  • 42.Sharma J, et al. Mice with Genetic Deletion of Group VIA Phospholipase A2beta Exhibit Impaired Macrophage Function and Increased Parasite Load in Trypanosoma cruzi-Induced Myocarditis. Infect Immun. 2016;84:1137–1142. doi: 10.1128/IAI.01564-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ishihara K, Kanai S, Tanaka K, Kawashita E, Akiba S. Group IVA phospholipase A(2) deficiency prevents CCl4-induced hepatic cell death through the enhancement of autophagy. Biochem Biophys Res Commun. 2016;471:15–20. doi: 10.1016/j.bbrc.2016.01.186. [DOI] [PubMed] [Google Scholar]
  • 44.Ishihara K, et al. Group IVA phospholipase A2 participates in the progression of hepatic fibrosis. Faseb J. 2012;26:4111–4121. doi: 10.1096/fj.12-205625. [DOI] [PubMed] [Google Scholar]
  • 45.Rodrigues GB, et al. Diethylcarbamazine: possible therapeutic alternative in the treatment of alcoholic liver disease in C57BL/6 mice. Clin Exp Pharmacol Physiol. 2015;42:369–379. doi: 10.1111/1440-1681.12369. [DOI] [PubMed] [Google Scholar]
  • 46.Bystrom J, et al. Endogenous epoxygenases are modulators of monocyte/macrophage activity. PLoS One. 2011;6:e26591. doi: 10.1371/journal.pone.0026591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Dai M, et al. Epoxyeicosatrienoic acids regulate macrophage polarization and prevent LPS-induced cardiac dysfunction. J Cell Physiol. 2015;230:2108–2119. doi: 10.1002/jcp.24939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Huang WC, Tsai PJ, Huang YL, Chen SN, Chuang LT. PGE2 production is suppressed by chemically-synthesized Delta7-eicosatrienoic acid in macrophages through the competitive inhibition of COX-2. Food Chem Toxicol. 2014;66:122–133. doi: 10.1016/j.fct.2014.01.031. [DOI] [PubMed] [Google Scholar]
  • 49.Horiguchi N, et al. Cell type-dependent pro- and anti-inflammatory role of signal transducer and activator of transcription 3 in alcoholic liver injury. Gastroenterology. 2008;134:1148–1158. doi: 10.1053/j.gastro.2008.01.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Zhang X, et al. Interleukin-6 is an important mediator for mitochondrial DNA repair after alcoholic liver injury in mice. Hepatology. 2010;52:2137–2147. doi: 10.1002/hep.23909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Holt MP, Cheng LL, Ju C. Identification and characterization of infiltrating macrophages in acetaminophen-induced liver injury. J Leukocyte Biol. 2008;84:1410–1421. doi: 10.1189/jlb.0308173. [DOI] [PMC free article] [PubMed] [Google Scholar]

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