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 Ly6C^hi and Ly6C^low
   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
   Ly6C^hi and Ly6C^low 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 hazard^[38]1. 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 hepatocytes^[39]2. Fat accumulation in hepatocytes
   (steatosis) is the earliest histopathological change in the liver
   associated with alcohol intake^[40]3. Continued steatosis results in
   hepatocyte death via apoptosis and necrosis which promotes inflammation
   and fibrosis formation^[41]4,[42]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
   architecture^[43]6. Persistent cirrhosis can ultimately lead to
   hepatocellular carcinoma and liver failure^[44]7.

   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
   patients^[45]8. 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 animals^[46]9,[47]10. Kupffer cells (KC), the
   liver resident macrophages, account for approximately 90% of the
   macrophage population in the healthy liver^[48]11. 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 response^[49]12. 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 injury^[50]13. Inflammation
   progresses with the chemotactic recruitment of Ly6C^+ monocytes to
   inflamed tissue that differentiate into Ly6C^hi infiltrating
   macrophages (IMs)^[51]13. During acute or chronic liver injury, the
   macrophage subtype promoting inflammation in the liver are Ly6C^hi
   monocyte-derived macrophages^[52]14,[53]15. Ly6C^hi 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 formation^[54]16–[55]19.
   While Ly6C^hi macrophages initially exert pro-fibrotic and
   pro-inflammatory function they can differentiate into Ly6C^low
   macrophages to facilitate tissue repair and inflammation
   resolution^[56]20,[57]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 diseases^[58]22. 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 development^[59]23,[60]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 Ly6C^hi and Ly6C^low 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 Ly6C^hi and Ly6C^low 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 Ly6C^hi and Ly6C^low 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 Ly6C^hi and Ly6C^low Infiltrating
Macrophages Following Ethanol Administration

   Infiltrating Ly6C^hi and Ly6C^low liver macrophage populations from
   ethanol fed mice were isolated by flow sorting (Fig. [61]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. [62]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 Ly6C^hi and Ly6C^low macrophages (Fig. [63]3). From the metabolite
   analysis, we observed 102 significantly altered metabolites between the
   macrophage subtypes (Table [64]1). In the lipid positive fraction, we
   detected 58 differentially regulated metabolites with 39 upregulated
   and 19 downregulated in the Ly6C^low compared to the Ly6C^hi
   macrophages. From the lipid negative fraction, we measured 30
   differentially regulated metabolites with 15 upregulated and 15
   downregulated in the Ly6C^low compared to the Ly6C^hi macrophages. In
   the aqueous fraction, we detected 14 differentially regulated
   metabolites with 8 being upregulated and 6 downregulated in Ly6C^low
   compared to the Ly6C^hi 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 [65]2). Ly6C^hi and
   Ly6C^low 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 [66]S1).

Figure 1.

   [67]Figure 1
   [68]Open in a new tab

   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, CD11b^hi F4/80^Int) from Kupffer cells (KC, CD11b^Int
   F4/80^hi). 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: Ly6C^hi and Ly6C^low.

Figure 2.

   [69]Figure 2
   [70]Open in a new tab

   Liver macrophage metabolomics and proteomics sample preparation.

Figure 3.

   [71]Figure 3
   [72]Open in a new tab

   Significantly alter metabolites between Ly6C^hi and Ly6C^low
   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 Ly6C^hi and Ly6C^low.

Table 1.

   Significantly altered metabolites between Ly6C^hi and Ly6C^low
   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
   [73]C01176
   MG(0:0/18:1/0:0) 4.13E-07 Up 13307.73 378.2748 2.5390 [74]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 [75]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
   [76]C03580
   CL(20:4/20:4/18:1/18:1) 0.022371477 Up 2.385773 1501.0281 5.2100
   [77]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
   [78]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 [79]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 [80]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 [81]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 [82]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 [83]C14771
   Vitamin A 0.03911776 Up 1.604065 286.2295 2.2710 [84]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
   [85]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 [86]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
   [87]C01897
   Imiquimod 0.04839949 Up 1.408694 240.1345 1.1220 HMDB14862
   Cycluron 0.039257277 Up 1.328519 220.1547 0.9920 [88]C19109
   7″-O-Phosphohygromycin 0.036477257 Up 1.277203 629.1862 2.9640
   [89]C03368
   Dodecanol 0.04339384 Down −1.38546 208.1831 1.1930 [90]C02277
   Aristolochic Acid 0.042970523 Down −1.49828 341.0521 1.1540 [91]C08469
   Ceramide (d18:1/22:0) 7.501 0.01739148 Down −1.61369 621.6092 7.5010
   [92]C00195
   Cer(d18:1/24:1) 0.013293305 Down −1.69156 647.6224 7.4810 [93]C00195
   Cer(d18:0/24:1) 0.026877573 Down −1.70931 649.638 7.8760 [94]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 [95]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 [96]C00195
   Ceramide (d18:1/20:0) 7.063001 0.021412965 Down −2.04623 593.5757
   7.0630 [97]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 [98]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
   [99]C01885
   MG(0:0/18:4/0:0) 0.015869742 Down −3.90034 350.2434 1.3730 [100]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 [101]C10391
   PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885 7.3700004
   [102]C00157
   PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431 [103]C02737
   PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986 [104]C00350
   PA(14:0/13:0) 0.018941188 Up 2.03645 614.3692 5.2680006 [105]C00416
   PE(20:2/P-18:1) 0.004769958 Up 1.966516 753.5574 7.0680003 [106]C00350
   PC(20:3/P-18:0) 0.010843969 Up 1.91783 795.6032 7.6989994 [107]C00157
   PE(14:0/22:1) 0.02230334 Up 1.846544 745.5694 5.279 [108]C00350
   PS(18:0/20:3) 0.010184665 Up 1.82344 813.5564 5.286 [109]C02737
   Ceramide (d18:1/22:0) 0.024829699 Up 1.654491 667.6106 7.5 [110]C00195
   Cer(d18:1/24:1) 0.03071489 Up 1.651767 693.6258 7.4820004 [111]C00195
   PE(24:0/P-16:0) 0.020011874 Up 1.637358 805.6088 7.485 [112]C00350
   PE(O-20:0/22:4) 0.04190944 Up 1.518955 809.6189 7.883001 [113]C13894
   PE(22:2/P-18:1) 0.026633823 Up 1.515987 781.5885 7.5039997 [114]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 [115]C00399
   PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357 5.2099996
   [116]C00157
   Phytosulfokine b 0.04650307 Down −1.43648 754.1618 1.097 HMDB29810
   Rimocidine 0.03588501 Down −1.43716 767.4112 3.0529997 [117]C15821
   Acetyl-N-formyl-5-methoxykynurenamine 0.033569902 Down −1.47063
   300.0885 1.156 [118]C05642
   alpha-Ribazole 0.04385764 Down −1.4749 314.104 1.2270001 [119]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 [120]C00350
   CL(18:0/18:1/18:1/18:0) 0.037032653 Down −1.59666 1461.0708 6.4690013
   [121]C05980
   CL(20:1/18:2/18:1/18:1) 0.024215354 Down −1.66882 1525.0492 5.209
   [122]C05980
   PC(14:1/P-18:0) 5.355 0.044275247 Down −1.72501 751.5351 5.355
   [123]C00157
   LysoPE(0:0/22:5) 0.049099866 Down −1.78763 509.2879 1.6539999
   [124]C05973
   PE(14:1/20:4) 0.009279267 Down −1.84186 709.4657 1.068 [125]C00350
   CL(18:0/18:0/18:2/18:0) 0.03732978 Down −2.06192 1457.063 5.2099996
   [126]C05980
   Camptothecin 0.01822235 Down −2.09969 348.1068 0.9259999 [127]C01897
   Aqueous Positive
   PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998 [128]C00157
   LysoPE(0:0/20:4) 4.39E-08 Up 8191.464 501.2852 1.5950001 [129]C05973
   PE(18:2/18:2) 8.68E-08 Up 6742.008 739.5146 2.8740003 [130]C00350
   Ceramide(d18:1/17:0) 0.033310328 Up 3.80966 551.5272 0.8509999
   [131]C00195
   Ceramide(d18:1/17:0) 0.84700006 0.030002557 Up 3.701004 533.5165
   0.8470001 [132]C00195
   CE(15:0) 0.033051185 Up 2.817563 609.5802 0.856 [133]C02530
   Hydroxybutyrylcarnitine 0.049097747 Up 2.040182 247.1433 5.395
   HMDB13127
   L-Carnitine 0.037892483 Up 1.340674 161.1052 5.8930006 [134]C00318
   Hydrocortisone caproate 0.04131846 Down −1.2559 442.272 0.7210001
   [135]C13422
   1,4′-Bipiperidine-1′-carboxylic acid 0.00321868 Down −1.31586 211.169
   1.3240001 [136]C16836
   Methylconiine 0.022564428 Down −1.32673 141.1508 1.441 [137]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
   [138]C01035
   5beta-Gonane 0.008270364 Down −2.18221 254.1995 2.3449998 [139]C19640
   [140]Open in a new tab

   (n = 3 in each experiment). Metabolites were considered significant
   with a fold change +/− 1.5 and ANOVA p < 0.05 when comparing Ly6C^hi
   and Ly6C^low.

Table 2.

   MBROLE functional pathway enrichment of significantly altered
   metabolites between Ly6C^hi and Ly6C^low 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
   [141]C05973 LysoPE(0:0/20:4) 0.00000004 Up 8191.464 501.2852 1.5950001
   [142]C05980 CL(20:4/20:4/18:1/18:1) 0.02237148 Up 2.385773 1501.028
   5.2099996
   [143]C05980 CL(16:0/18:1/18:1/18:0) 0.02935672 Up 2.216448 1433.044
   5.2099996
   [144]C02737 PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431
   [145]C00416 PA(14:0/13:0) 0.01894119 Up 2.03645 614.3692 5.2680006
   [146]C02737 PS(18:0/20:3(8Z,11Z,14Z)) 0.01018467 Up 1.82344 813.5564
   5.286
   [147]C05980 CL(18:0/18:1/18:1/18:0) 0.03703265 Down −1.59666 1461.071
   6.4690013
   [148]C05980 CL(20:1/18:2/18:1/18:1) 0.02421535 Down −1.66882 1525.049
   5.209
   [149]C05973 LysoPE(0:0/22:5) 0.04909987 Down −1.78763 509.2879
   1.6539999
   [150]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])
   [151]C00157 PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998
   [152]C00157 PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885
   7.3700004
   [153]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
   [154]C00157 PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357
   5.2099996
   [155]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])
   [156]C00157 PC(14:0/20:1) 1.17E-06 Up 14552.3 759.577 2.7959998
   [157]C00157 PC(20:3/P-18:1) 7.3700004 9.04E-09 Up 9632.273 793.5885
   7.3700004
   [158]C00350 PE(18:2/18:2) 8.68E-08 Up 6742.008 739.5146 2.8740003
   [159]C02737 PS(22:2/20:4) 0.00619864 Up 2.130906 863.5636 6.431
   [160]C00350 PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986
   [161]C00350 PE(20:1/20:3) 0.008398175 Up 2.125419 795.5765 6.4339986
   [162]C00416 PA(14:0/13:0) 0.01894119 Up 2.03645 614.3692 5.2680006
   [163]C00350 PE(20:2/P-18:1) 0.004769958 Up 1.966516 753.5574 7.0680003
   [164]C00157 PC(20:3/P-18:0) 0.010843969 Up 1.91783 795.6032 7.6989994
   [165]C00350 PE(14:0/22:1) 0.02230334 Up 1.846544 745.5694 5.279
   [166]C02737 PS(18:0/20:3) 0.01018467 Up 1.82344 813.5564 5.286
   [167]C00350 PE(24:0/P-16:0) 0.020011874 Up 1.637358 805.6088 7.485
   [168]C00350 PE(22:2/P-18:1) 0.026633823 Up 1.515987 781.5885 7.5039997
   [169]C00157 PC(14:1/P-18:0) 0.041162275 Down −1.38207 751.5357
   5.2099996
   [170]C00350 PE(14:0/16:0) 0.032299943 Down −1.52035 663.4833 5.243
   [171]C00157 PC(14:1/P-18:0) 5.355 0.044275247 Down −1.72501 751.5351
   5.355
   [172]C00350 PE(14:1/20:4) 0.009279267 Down −1.84186 709.4657 1.068
   [173]Open in a new tab

   (n = 3 in each experiment). Pathway enrichment was considered
   significantly with a MBROLE calculated p < 0.05.

Comparative Proteomic Analysis of Ly6C^hi and Ly6C^low 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 Ly6C^hi and Ly6C^low
   macrophages with 340 and 214 proteins, respectively, uniquely expressed
   between macrophage subtypes (Fig. [174]4A). The 1,304 protein found in
   the Ly6C^hi and Ly6C^low macrophages were subjected to DAVID analysis.
   From the 1,304 proteins analyzed, we observed 429 biological processes
   of which 105 were unique for Ly6C^low and 75 for Ly6C^high macrophages
   (Fig. [175]4B). Furthermore, we detected 200 molecular functions from
   the 1,304 proteins of which 23 are unique for Ly6C^low and 28 for
   Ly6C^high macrophages (Fig. [176]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 [177]S3–[178]S5). Protein quantitative analysis
   of significantly altered proteins was obtained from Mass Profiler
   Professional and we detected 47 differentially regulated proteins
   between the Ly6C^hi and Ly6C^low macrophages (Table [179]3). The
   significantly altered proteins between the Ly6C^hi and Ly6C^low
   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 [180]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 [181]4).

Figure 4.

   [182]Figure 4
   [183]Open in a new tab

   Venn Diagrams of unique protein, biological processes and molecular
   functions between Ly6C^hi and Ly6C^low macrophages. (A) Number of
   common and unique proteins between Ly6C^hi and Ly6C^low macrophages.
   (B) Number of common and unique biological processes between Ly6C^hi
   and Ly6C^low macrophages. (C) Number of common and unique molecular
   functions between Ly6C^hi and Ly6C^low macrophages. Lists of common and
   unique protein, biological processes and molecular functions can be
   found in Supplementary Tables [184]S3–[185]S5.

Table 3.

   Quantitative analysis of MS-only spectra of significantly altered
   proteins between Ly6C^hi and Ly6C^low macrophages.
   Quantitative Proteomics Analysis
   Protein Name Protein ID Peptide # p-value Fold Change (Low vs High)
   Regulation
   Phospholipase D3 [186]O35405 2 8.24E-09 32371.51 Up
   Cathepsin L1 [187]P06797 4 2.77E-08 5875.09 Up
   Ras-related protein Rap-1b [188]Q99JI6 2 4.55E-02 1512.80 Up
   Protein S100-A9 [189]P31725 8 4.06E-05 32.22 Up
   Protein S100-A8 [190]P27005 5 2.17E-04 31.14 Up
   Cathelin-related antimicrobial peptide [191]P51437 2 1.72E-05 28.13 Up
   H-2 class II histocompatibility antigen, A-B alpha chain [192]P14434 4
   4.57E-04 17.26 Up
   H-2 class II histocompatibility antigen, A beta chain [193]P14483 4
   1.85E-04 15.85 Up
   Lactotransferrin [194]P08071 14 4.63E-04 14.67 Up
   Neutrophil gelatinase-associated lipocalin [195]P11672 2 2.42E-03 14.47
   Up
   Macrophage asialoglycoprotein-binding protein 1 [196]P49300 4 3.92E-04
   6.48 Up
   H-2 class II histocompatibility antigen gamma chain [197]P04441 4
   7.13E-04 4.74 Up
   CD177 antigen [198]Q8R2S8 3 8.01E-04 4.70 Up
   Gelsolin [199]P13020 11 1.36E-03 3.17 Up
   Transcription factor A, mitochondrial [200]P40630 2 9.01E-03 3.09 Up
   Vasodilator-stimulated phosphoprotein [201]P70460 2 1.67E-02 2.91 Up
   EF-hand domain-containing protein D2 [202]Q9D8Y0 3 2.42E-02 2.79 Up
   Putative phospholipase B-like 1 [203]Q8VCI0 6 9.23E-03 2.54 Up
   Chitinase-3-like protein 3 [204]O35744 13 6.29E-03 2.54 Up
   Synaptosomal-associated protein 23 [205]O09044 2 4.40E-03 2.51 Up
   Low affinity immunoglobulin gamma Fc region receptor II [206]P08101 4
   1.73E-02 2.46 Up
   Histone H1.3 [207]P43277 2 1.11E-02 2.33 Up
   Lymphocyte-specific protein 1 [208]P19973 10 2.03E-02 2.15 Up
   C-type lectin domain family 4 member F [209]P70194 12 5.30E-03 2.12 Up
   Allograft inflammatory factor 1 [210]O70200 3 5.35E-03 2.11 Up
   Alpha-actinin-1 [211]Q7TPR4 20 5.04E-03 2.08 Up
   Hematopoietic lineage cell-specific protein [212]P49710 10 1.66E-02
   2.02 Up
   EF-hand domain-containing protein D1 [213]Q9D4J1 3 2.92E-02 2.02 Up
   Tyrosine-protein phosphatase non-receptor type substrate 1 [214]P97797
   2 4.56E-02 1.97 Up
   Histone H1.0 [215]P10922 2 1.22E-02 1.87 Up
   Annexin A1 [216]P10107 14 1.22E-02 1.86 Up
   Integrin alpha-L [217]P24063 4 1.58E-02 1.74 Up
   Prelamin-A/C [218]P48678 9 2.11E-02 1.52 Up
   ATP synthase subunit alpha, mitochondrial [219]Q03265 16 3.66E-02 1.49
   Up
   ATP synthase subunit beta, mitochondrial [220]P56480 25 4.22E-02 1.43
   Up
   Lysosome-associated membrane glycoprotein 1 [221]P11438 3 4.39E-02
   −1.45 Down
   Filamin-A [222]Q8BTM8 59 2.53E-02 −1.57 Down
   V-type proton ATPase subunit B, brain isoform [223]P62814 3 2.16E-02
   −1.79 Down
   Plectin [224]Q9QXS1 4 1.65E-02 −1.80 Down
   Proliferation-associated protein 2G4 [225]P50580 4 1.91E-02 −1.85 Down
   DNA-binding protein A [226]Q9JKB3 2 3.33E-02 −1.88 Down
   Glutathione S-transferase Mu 1 [227]P10649 8 2.57E-02 −1.90 Down
   Tubulin alpha-4A chain [228]P68368 3 1.92E-02 −1.98 Down
   Polyadenylate-binding protein 1 [229]P29341 5 6.88E-03 −2.01 Down
   Isocitrate dehydrogenase [NADP] cytoplasmic [230]O88844 4 4.32E-02
   −2.18 Down
   Lysozyme C-1 [231]P17897 2 8.32E-03 −2.27 Down
   Coagulation factor XIII A chain [232]Q8BH61 13 6.73E-03 −2.99 Down
   [233]Open in a new tab

   (n = 3 in each experiment). Protein were considered significant with a
   Mass Protein Profiler calculated ANOVA p < 0.05 when comparing Ly6C^hi
   and Ly6C^low.

Table 4.

   DAVID functional pathway enrichment of significantly altered proteins
   between Ly6C^hi and Ly6C^low 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
   [234]P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4
   0.00046 17.26 Up
   [235]P14483 H-2 class II histocompatibility antigen, A beta chain 4
   0.00019 15.85 Up
   [236]P04441 H-2 class II histocompatibility antigen gamma chain 4
   0.00071 4.74 Up
   [237]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
   [238]P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4
   0.00046 17.26 Up
   [239]P14483 H-2 class II histocompatibility antigen, A beta chain 4
   0.00019 15.85 Up
   [240]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
   [241]P13020 Gelsolin 11 0.00136 3.17 Up
   [242]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [243]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
   [244]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [245]P10107 Annexin A1 14 0.01223 1.86 Up
   [246]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
   [247]P13020 Gelsolin 11 0.00136 3.17 Up
   [248]P08101 Low affinity immunoglobulin gamma Fc region receptor II 4
   0.01726 2.46 Up
   [249]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [250]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
   [251]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [252]P27005 Protein S100-A8 5 0.00022 31.14 Up
   [253]P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4
   0.00046 17.26 Up
   [254]P14483 H-2 class II histocompatibility antigen, A beta chain 4
   0.00019 15.85 Up
   [255]P08071 Lactotransferrin 14 0.00046 14.67 Up
   [256]P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47
   Up
   [257]P04441 H-2 class II histocompatibility antigen gamma chain 4
   0.00071 4.74 Up
   [258]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
   [259]P14434 H-2 class II histocompatibility antigen, A-B alpha chain 4
   0.00046 17.26 Up
   [260]P14483 H-2 class II histocompatibility antigen, A beta chain 4
   0.00019 15.85 Up
   [261]P04441 H-2 class II histocompatibility antigen gamma chain 4
   0.00071 4.74 Up
   [262]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
   [263]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [264]P27005 Protein S100-A8 5 0.00022 31.14 Up
   [265]O35744 Chitinase-3-like protein 3 13 0.00629 2.54 Up
   [266]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [267]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
   [268]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [269]P27005 Protein S100-A8 5 0.00022 31.14 Up
   [270]P51437 Cathelin-related antimicrobial peptide 2 0.00002 28.13 Up
   [271]P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47
   Up
   [272]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
   [273]P08071 Lactotransferrin 14 0.00046 14.67 Up
   [274]P04441 H-2 class II histocompatibility antigen gamma chain 4
   0.00071 4.74 Up
   [275]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [276]Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
   [277]P50580 Proliferation-associated protein 2G4 4 0.01913 −1.85 Down
   [278]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
   [279]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [280]P27005 Protein S100-A8 5 0.00022 31.14 Up
   [281]P11672 Neutrophil gelatinase-associated lipocalin 2 0.00242 14.47
   Up
   [282]P13020 Gelsolin 11 0.00136 3.17 Up
   [283]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
   [284]P13020 Gelsolin 11 0.00136 3.17 Up
   [285]P70460 Vasodilator-stimulated phosphoprotein 2 0.01674 2.91 Up
   [286]P19973 Lymphocyte-specific protein 1 10 0.02033 2.15 Up
   [287]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [288]Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
   [289]P49710 Hematopoietic lineage cell-specific protein 10 0.01658 2.02
   Up
   [290]Q8BTM8 Filamin-A 59 0.02534 −1.57 Down
   [291]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
   [292]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [293]Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
   [294]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
   [295]P31725 Protein S100-A9 8 0.00004 32.22 Up
   [296]P27005 Protein S100-A8 5 0.00022 31.14 Up
   [297]P13020 Gelsolin 11 0.00136 3.17 Up
   [298]Q9D8Y0 EF-hand domain-containing protein D2 3 0.02416 2.79 Up
   [299]O70200 Allograft inflammatory factor 1 3 0.00535 2.11 Up
   [300]Q7TPR4 Alpha-actinin-1 20 0.00504 2.08 Up
   [301]Q9D4J1 EF-hand domain-containing protein D1 3 0.02919 2.02 Up
   [302]P10107 Annexin A1 14 0.01223 1.86 Up
   [303]P56480 ATP synthase subunit beta, mitochondrial 25 0.04219 1.43 Up
   [304]Open in a new tab

   (n = 3 in each experiment). Pathway enrichment was considered
   significant with a DAVID calculated t-test p < 0.05 when comparing
   Ly6C^hi and Ly6C^low.

Discussion

   Alcoholic liver disease is a major public health issue and accounts for
   approximately 48% of liver cirrhosis related deaths^[305]1. As
   infiltrating macrophages are known to mediate the pathogenesis of ALD
   from steatosis to cirrhosis^[306]8–[307]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 cells^[308]21,[309]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 transcriptomic^[310]26 and lipidomic^[311]26–[312]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, Ly6C^hi macrophages differentiate into Ly6C^low
   macrophages which express higher levels of phagocytosis related genes
   after alcohol intake^[313]21. 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.^[314]11,[315]21,[316]25 Therefore, the analysis done in this
   study was focused on the difference between Ly6C^hi and Ly6C^low
   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 [317]4). We
   detected an upregulation of phagocytosis related proteins in Ly6C^low
   macrophages; this is expected as phagocytosis of apoptotic cells
   induces an anti-inflammatory phenotype^[318]29,[319]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 Ly6C^low
   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 polarization^[320]31. Additionally, defects in actin
   polymerization have been shown to attenuate macrophage phagocytic
   ability^[321]32. 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 phenotypes^[322]33. In our study,
   we found a significant enrichment in glycerophospholpid metabolism with
   differential metabolite regulation between Ly6C^hi and Ly6C^low
   macrophages. Additionally, we observed enrichment for arachidonic acid
   metabolism and phospholipid biosynthesis (Table [323]2). In each of the
   enriched pathways, we detected a massive upregulation in multiple
   phosphatidylcholine (PC) species in Ly6C^low 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 macrophages^[324]34. Likewise, we observed a
   substantial upregulation in phosphatidylethanolamine (PE(18:2/18:2) in
   Ly6C^low 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
   phenotype^[325]35. 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 macrophages^[326]36. These results
   suggest the involvement of phospholipase A[2] (PLA[2]) in regulating
   macrophage polarization in ALD. PLA[2] is involved in the hydrolysis of
   sn-2 fatty acids from membrane glycerophospholipids yielding a free
   fatty acid, arachidonic acid, and a lysophospholipid^[327]37. The
   functions of PLA[2] in modulating the inflammatory response have been
   well established in a variety of inflammatory contexts^[328]38–[329]42.
   Ishihara et al. have shown that targeting cytosolic PLA[2] activity in
   non-alcoholic fatty liver disease models proved beneficial in
   preventing hepatic fibrosis formation and reducing hepatocyte
   death^[330]43,[331]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 ALD^[332]45. 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)^[333]46. We found a significant increase in EETs in the Ly6C^low
   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)^[334]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 signaling^[335]47. Finally, studies have shown
   that eicosatrienoic acid inhibits LPS induced inflammatory gene
   expression in macrophages^[336]48. We detected an upregulation of
   eicosatrienoic acid metabolites in the anti-inflammatory, Ly6C^low
   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 PLA[2] 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 macrophages^[337]26–[338]28,[339]33,[340]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 PLA[2]
   enzymes play a critical role in modulating macrophage phenotypes. To
   explore the impact of PLA[2] on ALD, future studies could utilize whole
   body PLA[2] knockout mice or known PLA[2] pharmacological inhibitors to
   elucidate the impact of PLA[2] 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
   previously^[341]49,[342]50.

Isolation of Liver Non-Parenchymal Cells (NPCs)

   Liver NPCs were isolated following a previously described
   method^[343]51. 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
   CaCl[2], 4 mM MgSO[4], 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, Ly6C^hi and Ly6C^low 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
   (Ly6C^hi and Ly6C^low). 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
   Ly6C^hi and Ly6C^low 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

   [344]Supplementary Data^ (1.1MB, pdf)

Acknowledgements