Abstract

   The miniature pig is a suitable animal model for investigating human
   cardiovascular diseases. Nevertheless, the alterations in lipid
   metabolism within atherosclerotic plaques of miniature pigs, along with
   the underlying mechanisms, remain to be comprehensively elucidated. In
   this study, we aim to examine the alterations in lipid composition and
   associated pathways in the abdominal aorta of atherosclerotic pigs
   induced by a high-fat, high-cholesterol, and high-fructose (HFCF) diet
   using lipidomics and RNA-Seq methods. The results showed that the
   content and composition of aortic lipid species, particularly ceramide,
   hexosyl ceramide, lysophosphatidylcholine, and triglyceride, were
   significantly altered in HFCF-fed pigs. Meanwhile, the genes governing
   sphingolipid metabolism, iron ion homeostasis, apoptosis, and the
   inflammatory response were significantly regulated by the HFCF diet.
   Furthermore, C16 ceramide could promote iron deposition in RAW264.7
   cells, leading to increased intracellular reactive oxygen species (ROS)
   production, apoptosis, and activation of the toll-like receptor 4
   (TLR4)/nuclear Factor-kappa B (NF-қB) inflammatory pathway, which could
   be mitigated by deferoxamine. Our study demonstrated that dysregulated
   ceramide metabolism could increase ROS production, apoptosis, and
   inflammatory pathway activation in macrophages by inducing iron
   overload, thus playing a vital role in the pathogenesis of
   atherosclerosis. This discovery could potentially provide a new target
   for pharmacological therapy of cardiovascular diseases such as
   atherosclerosis.

   Keywords: atherosclerosis, lipidomics, ceramide, iron, oxidative
   stress, miniature pigs

1. Introduction

   Cardiovascular disease remains the leading global cause of mortality,
   and atherosclerosis has been established as the primary underlying
   pathological mechanism [[40]1]. Atherosclerosis manifests as the
   buildup of lipids within arterial walls, culminating in macrophage
   migration and smooth muscle proliferation, resulting in arterial wall
   hardening, thickening, and loss of elasticity [[41]2]. Growing evidence
   indicates the pivotal role of aberrant lipid metabolism in the
   initiation and progression of atherosclerosis, potentially serving as a
   common thread linking various pathogenic factors, including obesity,
   hypertension, and diabetes [[42]3]. Lipid-lowering therapy is indicated
   for patients with cardiovascular diseases, including atherosclerosis.
   Statins and proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors
   constitute the principal means to reduce serum low-density lipoprotein
   cholesterol (LDL-C) levels in clinical atherosclerosis treatment.
   Nevertheless, many patients fail to attain ideal lipid levels even with
   medication, potentially encountering drug intolerance and associated
   risks [[43]4]. Hence, identifying new intervention targets related to
   abnormal lipid metabolism could offer fresh insights for preventing and
   treating atherosclerosis and atherosclerotic cardiovascular diseases.

   Lipidomic analysis, based on mass spectrometry, has emerged as a potent
   tool for investigating lipid composition in biological samples during
   disease states. This approach enables us to reveal the mechanisms
   underpinning lipid-related diseases such as atherosclerosis by
   quantifying changes in specific lipid categories [[44]5]. Current
   clinical research on atherosclerosis mainly involves identifying lipid
   biomarkers from blood samples, thereby inferring their correlation with
   atherosclerosis due to the challenges associated with actual plaque
   sampling [[45]6,[46]7]. However, recent studies have revealed no
   significant correlation between arterial plaque tissue and plasma
   metabolites in atherosclerotic patients and mouse models [[47]8,[48]9].
   Consequently, it is essential to directly assess changes in lipid
   metabolites within atherosclerotic plaques. It is now understood that
   miniature pigs closely resemble humans in cardiovascular anatomical
   structure and lipoprotein composition, offering distinct advantages for
   cardiovascular disease research, including atherosclerosis.
   Nevertheless, there is a dearth of reports regarding the alterations in
   lipid composition within atherosclerotic plaque in miniature pigs.
   Additionally, atherosclerosis represents a complex multifactorial
   disease, with oxidative stress, inflammation, and apoptosis playing
   pivotal roles in its onset and progression. However, the intricate
   interplay among these key factors remains largely uncharted [[49]10].
   Therefore, when studying atherosclerosis in animal models, integrating
   lipidomics with additional omics methods like transcriptomics can help
   elucidate the potential mechanisms governing specific changes in lipid
   metabolism and their interaction with related molecular targets.

   Herein, we conducted untargeted lipidomic profiling of atherosclerotic
   aortas in Wuzhishan miniature pigs subjected to either a normal chow
   (NC) diet or a high-fat, high-cholesterol, and high-fructose (HFCF)
   diet for six months. Moreover, transcriptome sequencing was employed to
   investigate the potential mechanisms behind HFCF diet-induced aortic
   ceramide accumulation, apoptosis, and inflammation in atherosclerotic
   pigs. Furthermore, in vitro experiments demonstrated the role of the
   long-chain C16 ceramide in macrophage iron overload and the activation
   of downstream pathways.

2. Materials and Methods

2.1. Animals

   Ten male Wuzhishan miniature pigs aged 6–7 months were acquired from
   the Institute of Animal Sciences, Hainan Academy of Agricultural
   Sciences (Haikou, China), in this study. Each pig was individually
   housed and had a 12 h light and dark cycle. Before the experiment, the
   pigs were fed a regular diet and had free access to water. After 4
   weeks of acclimatization, the animals were divided into two groups and
   fed either an NC (3.2 kcal·g^−1, 22% of kcal from protein, 8% of kcal
   from fat, and 70% of kcal from carbohydrate) or an HFCF (4.5 kcal·g^−1,
   12% of kcal from protein, 46% from fat, and 42% from carbohydrate) diet
   for 6 months. Each group consisted of five animals. The HFCF diet
   consisted of 15% shortening oil, 10% egg yolk power, 1.5% cholesterol,
   10% fructose, and 63.5% normal chow diet. The pigs in each group were
   fed twice daily with a total daily amount equivalent to 2.5% of their
   body weight. The Laboratory Animal Management and Use Committee of the
   Zhejiang Chinese Medical University (Hangzhou, China, permit no.
   ZSLL-2016-0013) authorized all procedures for this study. A diagram of
   the animal experiment is shown in [50]Figure 1.

Figure 1.

   [51]Figure 1
   [52]Open in a new tab

   The flow diagram of the animal experiment. NC, normal chow; HFCF,
   high-fat, high-cholesterol, and high-fructose diet; H&E, hematoxylin
   and eosin; IHC, immunohistochemistry; IF, immunofluorescence; TUNEL,
   terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling;
   PCA, principal component analysis; and OPLS-DA, orthogonal partial
   least-squares-discriminant analysis.

   The body weight of the pigs was recorded monthly. After 6 months of
   feeding, the pigs were killed by exsanguination under anesthesia. Blood
   samples were collected and centrifuged at 3000× g for 15 min. The serum
   was separated and stored at −80 °C for further analysis. The entire
   aorta, from the aortic arch to the iliac artery, was removed and fixed
   in 10% buffered formalin for 24 h. The samples were then stained with
   Sudan IV dye for 15 min, rinsed, and then photographed. Image J
   software (NIH, version 1.6.0) was used to analyze lipid deposition in
   the whole aorta.

2.2. Biochemical Parameter Measurements

   Biochemical markers associated with lipid metabolism, including
   triglycerides (TG), total cholesterol (TC), high-density lipoprotein
   cholesterol (HDL-C), and LDL-C, were measured with an automatic
   biochemistry analyzer (Hitachi 7020; Hitachi, Tokyo, Japan). Iron
   content in the abdominal aorta tissues was determined with an iron
   detection kit (TC1015, Leagene, Beijing, China). Briefly, 20 mg of
   abdominal aorta was homogenized, and the protein concentration was
   measured using a bicinchoninic acid (BCA) protein assay kit. The
   absorbance of the collected samples was measured at the wavelength of
   562 nm using a microplate reader, and the concentration of iron was
   calculated accordingly [[53]11].

2.3. Histological Analysis

   The abdominal aorta was fixed in 10% formalin, embedded in paraffin,
   sectioned at 4 μm thickness, and stained with hematoxylin and eosin
   (H&E). The intima to media thickness ratio was measured with Image J
   software (NIH, version 1.6.0). The sections were examined via a light
   microscope (Nikon Eclipse 80i; Nikon, Tokyo, Japan). Apoptosis in
   abdominal aorta tissues was assessed using a TUNEL BrightGreen
   Apoptosis Detection Kit (A112-01, Vazyme, Nanjing, China) according to
   the manufacturer’s instructions. The apoptotic cells were labeled with
   green immunofluorescence, while the cell nuclei were stained with DAPI.
   In brief, following rehydration with a xylene and ethanol series, the
   slides were treated with 20 μg/mL of proteinase K and incubated at 37
   °C for 20 min. They were then rinsed with 1× phosphate buffer solution
   (PBS) for 5 min, three times. Subsequently, the slides were exposed to
   50 μL of TUNEL detection solution and incubated at 37 °C for 60 min,
   followed by three washes with 1 × PBS. Finally, they were stained with
   an anti-fluorescence quenching solution. The rate of apoptotic cells
   was calculated as the ratio of TUNEL-positive nuclei to DAPI-stained
   nuclei. Movat staining was employed to examine foam cells and collagen
   fibers in the plaque. Image-Pro Plus 6.0 Software was used for area
   quantification.

2.4. Lipid Sample Preparation and Lipidomic Profiling

   Lipids were extracted from abdominal aorta tissues using the methyl
   tert-butyl ether (MTBE) method as previously described [[54]12].
   Briefly, 30 mg of tissue was homogenized in 200 μL of ice-cold water
   and 240 μL of methanol, followed by 800 μL of MTBE. The samples were
   ultrasonically treated for 20 min at 4 °C, then allowed to rest for 30
   min at room temperature. Lipids were extracted by centrifuging the
   solution at 10 °C for 15 min (14,000× g) to extract lipids. The upper
   organic layer was collected, and the solvent was evaporated under
   nitrogen and stored at −80 °C until analysis.

   LC-MS/MS analysis was performed using a Q Exactive plus mass
   spectrometer (Thermo Scientific, Waltham, MA, USA) coupled to a UHPLC
   Nexera LC-30A system (Shimadzu, Tokyo, Japan). Briefly, lipids were
   separated using a Waters Acquity UHPLC system with a CSH C18 column
   (2.1 × 100 nm, 1.7 μm). MS analysis was performed using electrospray
   ionization (ESI) positive and negative ions. Positive and negative ion
   full-scan spectra were collected within mass-to-charge ratio (m/z)
   ranges of 200–1800 and 250–1800, respectively. Following each full
   scan, 10 fragment patterns (MS2 scan, HCD) were collected to determine
   the mass-to-charge ratio of lipid molecules. The initial lipidomic data
   were processed using LipidSearch software version 4.1 (Thermo
   Scientific^TM, Waltham, MA, USA) for peak recognition, lipid
   identification (secondary identification), peak extraction, peak
   alignment, and quantification. Among the extracted ion features, only
   variables with at least 50% nonzero values were retained. Unsupervised
   multivariate analyses and fatty acyl chain composition analyses were
   carried out on identified lipids. Samples from each group were pooled
   equally to create a pooled quality control (QC). The experimental
   process included QC samples to evaluate system stability and data
   reliability.

2.5. Unsupervised Multivariate Data Analyses

   Multivariate statistical analysis was conducted using SIMCA-P 16.0
   software (Umetrics, Umea, Sweden). Following the Pareto scaling,
   principal component analysis (PCA) and orthogonal partial
   least-squares-discriminant analysis (OPLS-DA) were performed. By
   combining the VIP value obtained from the OPLS-DA model with a
   two-tailed Student’s t-test, significant differential metabolites were
   determined. Moreover, metabolites with VIP values greater than 1.0 and
   p values less than 0.05 were significant and considered differentially
   expressed. Hierarchical cluster analysis was performed with R software
   (version 3.5.1).

2.6. cDNA Library Construction and RNA-Seq Analysis

   Pigs from each group were used for RNA sequencing. TRIzol reagent
   (Invitrogen, Carlsbad, CA, USA) was used to isolate total RNA from
   abdominal aorta tissues. Quantity and purity of RNA were assessed using
   the Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) and Animal Tissue
   RNA Purification Kit (LC Sciences, Houston, TX, USA), respectively.

   RNA samples of high quality were used for cDNA library construction.
   More detailed information on the cDNA library construction, paired-end
   sequencing, quality control, reads mapping, and the fragments per
   kilobase of exon per million reads mapped (FPKM) calculations can be
   found in our previous studies [[55]13,[56]14]. Using the Illumina
   paired-end RNA-Seq approach, we sequenced the transcriptome, generating
   a total of a million 2 × 150 bp paired-end reads. We aligned reads of
   all samples to the sus scrofa reference genome (Ssscrofa 10.2) using
   the HISAT2 ([57]https://daehwankimlab.github.io/hisat2/, accessed on 27
   March 2021) package. The mapped reads of each sample were assembled
   using StringTie ([58]http://ccb.jhu.edu/software/stringtie/, accessed
   on 27 March 2021). Differentially expressed genes (DEGs) were selected
   based on a fold change > 2 or <0.5 with statistical significance
   determined with a p-value < 0.05 using R language. The raw data were
   deposited into the Gene Expression Omnibus (GEO) database under
   accession number [59]GSE245530.

2.7. Bioinformatic Analysis

   Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway enrichment analysis were applied to analyze the
   main function of DEGs according to the NCBI Gene Ontology database. An
   adjusted p-value < 0.05 was set as the threshold for significantly
   enriched GO terms and KEGG pathways. We performed gene set enrichment
   analysis using the software Gene Set Enrichment Analysis (GSEA, version
   4.3.2) to identify whether a set of genes in Reactome shows significant
   differences in the two groups. The normalized enrichment score (NES) >
   1, nominal (NOM) p-value < 0.05, and false discovery rate (FDR) q-value
   < 0.25 were used to highlight significant pathways of enrichment.

2.8. Cell Culture

   RAW264.7 cells were purchased from the Cell Resource Center of the
   Chinese Academy of Medicinal Sciences (Beijing, China). RAW264.7 cells
   were cultured at 37 °C in a 5% CO[2] atmosphere. The DMEM was
   supplemented with 10% fetal bovine serum and contained 100 U/mL of
   penicillin and 100 μg/mL of streptomycin. RAW264.7 cells were seeded in
   triplicates in 96-well plates at a certain density (2 × 10^5
   cells/well). Then, the cells were treated with different concentrations
   of C16 ceramide (860516P, Avanti Polar Lipids) (1, 10, 100, and 200 μM)
   for 24 h. After 24 h of incubation, 50 μL of MTT reagent was added to
   each well and cultured in an incubator for 4 h in the dark. The
   supernatant was removed, and the optical density of the formazan
   solution was measured by using a microplate reader at 570 nm.

   To investigate the impact of C16 ceramide on cellular iron homeostasis
   and function, RAW264.7 cells underwent different treatments, including
   PBS, 100 μM C16 ceramide, and 50 μM deferoxamine (DFO) (HY-B0988, MCE).
   For the measurement of reactive oxygen species (ROS), cells from the
   various groups were first washed twice with PBS and then incubated with
   the 2′,7′-dichlorodihydro-fluorescein diacetate (DCFH-DA) probe (10
   μmol/L) (S0033S, Beyotime, Shanghai, China) for 20 min at 37 °C. The
   fluorescence intensity was subsequently determined at an excitation
   wavelength of 488 nm and an emission wavelength of 525 nm using a
   fluorescence microscope. For flow cytometry analysis, cells were
   harvested and washed twice in ice-cold PBS after 24 h of treatment.
   Then, the cells were resuspended in 100 μL of 1 × Binding Buffer
   provided in the kit. To each tube, 5 μL of Annexin V-PE and 5 μL of
   7-AAD were added, mixed gently, and incubated for 15 min in the dark at
   room temperature. Finally, 400 μL of 1 × Binding Buffer was added to
   the samples, and they were promptly subjected to flow cytometry
   analysis (Beckman Coulter, Brea, CA, USA).

2.9. Quantitative Real-Time PCR Analysis

   Real-time PCR analysis was performed as previously described [[60]14].
   Briefly, total RNA was isolated from abdominal aorta tissues using
   TRIzol reagent (Invitrogen). One microgram of total RNA was
   reverse-transcribed using random primers and MMLV reverse transcriptase
   (Promega, Madison, WI, USA). Quantitative Real-time PCR (qRT-PCR) was
   carried out using the StepOnePlus Real-Time PCR Detection System
   (Applied Biosystems, Foster City, CA, USA). Relative expression levels
   of genes were analyzed using the 2^−∆∆CT method. The primers used are
   listed in [61]Supplementary Materials, S1: Table S1.

2.10. Western Blot Analysis

   Western blotting was conducted as previously described [[62]13,[63]14].
   Briefly, total protein was isolated from frozen abdominal aorta
   tissues. Then, the cells were lysed using RIPA buffer and further
   quantified using a BCA protein assay Kit (KGP2100, Keygen, China).
   Next, the proteins were subjected to SDS-PAGE and electrotransferred
   onto PVDF membranes (Bio-Rad, Hercules, CA, USA). Then, the membranes
   were incubated with primary antibodies overnight at 4 °C, followed by
   the corresponding secondary antibodies (Li-COR Biosciences, Lincoln,
   NE, USA) for 1 h at room temperature. The primary antibodies used were
   as follows: β-actin (4970S, Cell Signaling Technology, CST, Boston, MA,
   USA), Caspase-3 (9662S, CST, USA), Caspase-8 (4790T, CST, Boston, USA),
   Caspase-9 (9508S, CST, Boston, MA, USA), NF-қB p65 (8242S, CST, Boston,
   MA, USA), Phospho-NF-қB p65 (Ser536) (3033T, CST, Boston, MA, USA),
   TLR2 (13744S, CST, USA), TLR4 (A00017S441, Boster, Wuhan, China), p38
   MAPK (9212S, CST, USA), Phospho-p38 MAPK (Thr180/Tyr182) (4613S, CST,
   Boston, MA, USA), BCL-2 (ab32124, Abcam, Cambridge, MA, USA), Bax
   (ab32503, Abcam, Cambridge, MA, USA), and TFRC (ab269513, Abcam,
   Cambridge, MA, USA). The target protein bands were visualized using the
   Odyssey Infrared Imaging System (Li-COR Biosciences, USA).

2.11. Immunohistochemistry and Immunofluorescence Staining

   As previously described [[64]13], the expression of NF-қB p65 (6956T,
   CST, Boston, MA, USA), α-SMA (ab5694, Abcam, Cambridge, MA, USA), CD68
   (ab201340, Abcam, Cambridge, MA, USA), TFRC (ab269513, Abcam,
   Cambridge, MA, USA), and FTL (ab269513, Abcam, Cambridge, MA, USA) in
   the abdominal aorta tissues were assessed via immunostaining. Briefly,
   sections were blocked with a 3% hydrogen peroxide (H[2]O[2]) to block
   the endogenous peroxidase. Then, the sections were incubated overnight
   with primary anti-NF-қB p65, anti-α-SMA, anti-TFRC, and anti-FTL
   antibodies at 4 °C. Next, washing was performed and the secondary
   antibody was incubated for 1 h at room temperature. After washing,
   sections were counterstained with hematoxylin and visualized with a
   diaminobenzidine (DAB) kit (Vector Laboratories, Burlingame, CA, USA).
   For immunofluorescence staining, the sections were incubated with
   primary anti-CD68 overnight at 4 °C. Afterwards, the sections were
   incubated with the appropriate fluorescently labeled secondary
   antibodies for 1 h at room temperature. After washing, DAPI (Roche) was
   used to stain the nuclei for 8 min. Images were taken with a DS-Fil
   digital camera mounted on a Nikon Eclipse 80i fluorescence microscope
   (Nikon).

2.12. Statistical Analysis

   GraphPad Prism 8.0 (Graphpad Software Inc., San Diego, CA, USA) was
   used for statistical calculations and data plotting. Statistical
   differences between two groups of unpaired design were determined using
   the Student’s t-test. The comparison among multiple groups was
   conducted using one-way analysis of variance (ANOVA) and followed by
   Tukey’s test. The results are presented as mean ± standard error. A
   p-value of less than 0.05 was considered statistically significant.

3. Results

3.1. Phenotype of Atherosclerotic Pigs Induced by High-Fat, High-Cholesterol,
and High-Fructose Diet

   Pigs on the HFCF diet exhibited significant weight gain (29.9 ± 5.1 kg
   vs. 14.2 ± 4.8 kg) and higher serum cholesterol levels, including TC (p
   < 0.01) and LDL-C (p < 0.01), compared with the pigs in the normal chow
   group ([65]Figure 2A,B,D). No significant differences in TG
   concentrations were observed between the HFCF and NC groups. Sudan IV
   staining revealed a substantial increase in atherosclerotic lesion
   areas in HFCF-fed pigs ([66]Figure 2F,G). Histological examination of
   H&E sections showed that the intimal area and intima-media thickness of
   aortas in the HFCF group were 13.0- and 1.5-fold higher than those in
   the NC group (p < 0.05) ([67]Figure 2H–J). Distinctive features of
   atherosclerosis, including foam cell infiltration, necrotic core
   formation, and smooth muscle cell proliferation, were observed in
   arterial tissues of the HFCF group ([68]Figure 2H). Sections were
   stained with Movat pentachrome to assess intimal and medial areas and
   plaque classification, while α-SMA staining confirmed collagen
   deposition and smooth muscle cell migration ([69]Figure 2K,L).
   Moreover, increased CD68-positive staining was observed in the aorta of
   the HFCF group (p < 0.01) ([70]Figure 2M).

Figure 2.

   [71]Figure 2
   [72]Open in a new tab

   Phenotype of atherosclerotic pigs induced by a high-fat,
   high-cholesterol, and high-fructose diet. (A) Body weight of pigs
   during the dietary intervention time; (B) serum total cholesterol; (C)
   serum high-density lipoprotein cholesterol; (D) serum low-density
   lipoprotein cholesterol; (E) serum triglyceride; (F) representative
   photographs of Sudan-IV-stained aorta; (G) quantification of the
   Sudan-IV-positive lesion area in en face aorta; (H) H&E staining of the
   abdominal aorta; scale bars, 1 mm; (I) mean plaque size of abdominal
   aortas; (J) intima-media thickness of abdominal aortas; (K)
   representative photomicrographs and quantification of Movat staining in
   sections of the abdominal aorta; (L) representative photomicrographs
   and quantification of α-SMA immunohistochemistry in sections of the
   abdominal aorta; and (M) representative photomicrographs and
   quantification of CD68 staining in sections of the abdominal aorta.
   Green, CD68; blue, DAPI. Original magnification, ×50. NC, normal chow;
   HFCF, high-fat, high-cholesterol, and high-fructose diet. All data were
   assessed using Student’s t-test and are presented as mean ± SEM, n = 5
   per group. * p < 0.05, ** p < 0.01, *** p < 0.001.

3.2. Alteration in Aortic Lipidomics in Atherosclerotic Pigs Induced by
High-Fat, High-Cholesterol, and High-Fructose Diet

   To explore changes in the overall lipid composition and distribution in
   the aorta of atherosclerotic pigs fed the HFCF diet, we conducted mass
   spectrometry-based lipidomic analysis on abdominal aorta tissues from
   both the HFCF and the NC groups. The detected lipid classes and their
   abbreviations are listed in the [73]Supplementary Materials, S1: Table
   S2 and S2: Table S3. We identified more than 796 distinct lipid species
   in abdominal aorta tissue, comprising 197 phosphatidylcholines (PCs),
   166 triacylglycerols (TGs), 104 phosphatidylethanolamines (PEs), 92
   sphingomyelins (SMs), 63 ceramides (Cers), 25 lysophosphatidylcholines
   (LPCs), 20 hexosyl ceramides (CerG1), and other lipid classes
   ([74]Figure 3A). Using OPLS-DA, we identified two separate groups (HFCF
   vs. NC; positive ion mode: R^2X = 0.834, R^2Y = 0.999, and Q2 = 0.897;
   negative ion mode: R^2X = 0.852, R^2Y = 0.998, and Q2 = 0.900;
   [75]Supplementary Materials, S1: Figure S1). Approximately 23% of the
   identified lipid species exhibited significant changes, with 139 lipid
   species increased and 42 decreased. Grouping the quantification results
   based on lipid categories, we observed a significant increase in the
   total abundance of fatty acyls and sphingolipids. In contrast, the
   abundance of glycerolipids, glycerophospholipids, saccharolipids,
   sterol lipids, and prenol lipids remained unaltered. A more detailed
   analysis of lipid subclasses showed increased levels of acylcarnitine
   (AcCa), fatty acids (FA), Cer, CerG1, CerG3, and TG in the HFCF group
   ([76]Figure 3B,C). Additionally, LPC and lysophosphatidylethanolamine
   (LPE) levels in glycerophospholipids in the HFCF group tended to be
   higher than in the NC group (p = 0.069 and 0.079). To further
   investigate individual lipid species regulated by the HFCF diet,
   significantly altered lipid species were visualized in a bubble plot
   ([77]Figure 3D).

Figure 3.

   [78]Figure 3
   [79]Open in a new tab

   Changes in the overall lipid composition and distribution in the aorta
   of atherosclerotic pigs fed a high-fat, high-cholesterol, and
   high-fructose diet. (A) Distribution of lipid classes considered for
   subsequent analysis in all of the samples detected via LC-MS/MS; (B)
   the intensity fold change in fatty acyls, glycerolipids,
   glycerophospholipids, sphingolipids, saccharolipids, and prenol lipids;
   (C) the intensity fold change in AcCa, FA, Cer, CerG1, CerG3, and TG;
   only lipids with p < 0.05 are displayed; and (D) bubble plot of altered
   lipid species in the aorta of atherosclerotic pigs fed a HFCF diet.
   Each dot represents a lipid species, and the dot size indicates
   significance. NC, normal chow; HFCF, high-fat, high-cholesterol, and
   high-fructose diet; ns, no significant. All data were assessed using
   Student’s t-test and are presented as mean ± SEM, n = 5 per group. * p
   < 0.05, ** p < 0.01.

   We employed hierarchical clustering heatmaps to illustrate changes in
   lipid subclasses of sphingolipids and glycerophospholipids ([80]Figure
   4A). In comparison with the NC group, we observed a significant
   increase in twenty-six ceramides, including Cer d18:0/16:0, Cer
   d18:0/18:0, Cer d18:0/22:0, Cer d18:1/14:0, Cer d18:1/18:0, Cer
   d18:1/20:0, Cer d18:1/22:0, Cer d18:1/24:0, Cer d18:1/24:1, and Cer
   d18:2/20:0, as well as twenty sphingomyelins, including SM d34:0, SM
   d36:0, and SM d40:0, in the abdominal aorta of HFCF-fed pigs
   ([81]Figure 4A). Seven LPC lipid species, such as LPC (16:0e), LPC
   (16:1), LPC (18:0), LPC (18:0e), LPC (18:1), and LPC (18:2), and two
   LPE lipid species, including LPE (20:4) and LPE (22:4), were also
   significantly elevated in the abdominal aorta of the HFCF group
   ([82]Figure 4A).

Figure 4.

   [83]Figure 4
   [84]Open in a new tab

   Comparison of significantly altered lipids in the atherosclerotic aorta
   of pigs and other species. (A) Heatmap showing the differentially
   expressed sphingolipid and glycerophospholipid subclasses; (B) Log2
   (fold change) values of significantly altered lipids in the
   atherosclerotic aorta of humans, pigs, and APOE^−/− mice; and (C)
   pairwise comparisons of significantly altered lipids are shown with a
   color gradient denoting Spearman’s correlation coefficient. NC, normal
   chow; HFCF, high-fat, high-cholesterol, and high-fructose diet; IMT,
   intima-media thickness; LDR, lipid deposition rate; and AI,
   atherosclerotic index. All data were assessed using Student’s t-test
   and are presented as mean ± SEM, n = 5 per group. * p < 0.05, ** p <
   0.01, *** p < 0.001.

   We analyzed lipid changes in plaques from atherosclerotic pigs,
   APOE^−/− mice, and human patients. We found five common lipid species,
   including Cer (d18:0/16:0), Cer (d18:1/16:0), Cer (d18:1/24:1), LPC
   (18:0), and LPC (18:1), which were significantly increased in
   atherosclerotic plaques ([85]Figure 4B). We also analyzed the
   correlation between these common lipids and atherosclerotic parameters
   and found positive correlations with serum cholesterol content,
   intima–media thickness, plaque area, and, intriguingly, plaque iron
   content, especially with Cer (d18:1/16:0) (C16 Cer) ([86]Figure 4C),
   suggesting that ceramides might contribute to atherosclerosis by
   influencing iron deposition.

   Maintaining cell membrane homeostasis is crucial for cardiovascular
   health, with the carbon chain length and degree of saturation of fatty
   acyls in the membrane lipids influencing membrane biophysical
   properties [[87]15]. We further analyzed the fatty acyl chain profile
   associated with sphingolipids. Our data indicated that the HFCF diet
   led to a substantial alteration in the fatty acyl chain profile of
   ceramides, CerG1, and CerG3, with increased levels of long-chain fatty
   acyls with 34C and polyunsaturated fatty acyls with low double bond
   numbers (1~2 double bonds) in the abdominal aorta tissue of pigs
   ([88]Supplementary Materials, S1: Figure S2). Regarding the effects of
   the HFCF diet on sphingolipid composition, we observed an increase in
   short-chain fatty acyls (16C) and medium-length (24C) fatty acyls in
   the abdominal aorta tissue. Most of the fatty acyl chain profiles of
   sphingomyelins remained unaltered due to HFCF diet feeding
   ([89]Supplementary Materials, S1: Figure S2). These findings suggest
   that the fatty acyl chains associated with sphingolipids undergo
   dynamic changes after HFCF diet feeding.

3.3. Significantly Increased Ceramide Metabolism, Inflammation, and
Apoptosis-Related Genes Were Observed in the Aorta of Atherosclerotic Pigs
Induced by High-Fat, High-Cholesterol, and High-Fructose Diet

   We applied RNA sequencing (RNA-Seq) to map the transcriptional
   alterations in the abdominal aorta tissue. We identified 3396 DEGs,
   including 2652 upregulated and 744 downregulated genes
   ([90]Supplementary Materials, S2: Table S4).

   For a comprehensive understanding of the functions of these DEGs, we
   conducted GO and KEGG pathway analyses. KEGG pathway analysis revealed
   significant enrichment in several immune and inflammatory response
   pathways, including the chemokine signaling pathway, mitogen-activated
   protein kinase (MAPK) signaling pathway, toll-like receptor signaling
   pathway, NF-kappa B signaling pathway, and NOD-like receptor signaling
   pathway. Interestingly, multiple lipid metabolism pathways, such as
   steroid biosynthesis, sphingolipid signaling pathway, sphingolipid
   metabolism, glycerophospholipid metabolism, and arachidonic acid
   metabolism, were also significantly enriched. Moreover, other pathways,
   including apoptosis and ferroptosis, exhibited significant enrichment
   ([91]Figure 5A, [92]Supplementary Materials, S2: Table S5). GO
   annotation revealed significantly enriched biological processes in the
   aorta of the HFCF group of pigs, encompassing inflammatory response,
   positive regulation of MAP kinase activity, positive regulation of the
   MAPK cascade, toll-like receptor 4 signaling pathway, response to
   lipid, regulation of the apoptotic process, reactive oxygen species
   metabolic processes, iron ion homeostasis, and cellular iron ion
   homeostasis ([93]Supplementary Materials, S2: Table S6). We further
   performed GSEA to identify the biological processes in atherosclerotic
   pigs. The results revealed significant changes in sphingolipid
   metabolism, iron uptake and transport, toll-like receptor cascades, and
   apoptosis ([94]Figure 5B, [95]Supplementary Materials, S1: Figure S3).
   Taken together, these results indicate that HFCF diet feeding can lead
   to alterations in multiple signaling pathways in the abdominal aorta
   tissue of pigs.

Figure 5.

   [96]Figure 5
   [97]Open in a new tab

   Alterations in the aortic transcriptional profiles of atherosclerotic
   pigs fed a high-fat, high-cholesterol, and high-fructose diet. (A) KEGG
   pathway analysis showing the enrichment of functional categories. The
   size of shapes indicates gene counts. The dot color indicates
   significance, and corresponding significance values are displayed as
   log10 (p-value). (B) Gene set enrichment analysis showing significant
   enrichment in sphingolipid metabolism. (C) Heatmap showing the
   ceramide-generation-related genes. The greater the log2 (fold change),
   the higher the significance. (D–G) qRT-PCR analysis of CERS6, ASAH1,
   SGMS1, and SGPP1 (n = 5). (H) relative expression levels of selected
   genes involved in the NF-қB signaling pathway from the RNA-Seq dataset;
   (I) relative expression levels of selected genes involved in apoptosis
   from the RNA-Seq dataset. (J) The quantification of TUNEL-positive
   cells (n = 3). (K) The quantification of NF-қB-positive cells (n = 4).
   (L) Representative photographs of TUNEL-stained aorta of the NC and
   HFCF groups of pigs. Green, TUNEL; blue, DAPI. Scale bars, 200 and 1000
   μm. (M) Representative photomicrographs of NF-қB staining in sections
   of the abdominal aorta in the NC and HFCF groups of pigs. Scale bars,
   100 μm. NC, normal chow; HFCF, high-fat, high-cholesterol, and
   high-fructose diet. All data were assessed using Student’s t-test and
   are presented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001,
   **** p < 0.0001.

   We generated heat maps to depict the expression of genes enriched in
   sphingolipid metabolism. Among these genes, ceramide synthase 6
   (CERS6), sphingomyelin synthase 1 (SGMS1), neutral ceramidase 1
   (ASAH1), sphingosine-1-phosphate phosphatase 1 (SGPP1),
   sphingosine-1-phosphate lyase 1 (SGPL1), and galactosylceramidase
   (GALC) displayed high expression levels in the abdominal aorta of
   atherosclerotic pigs ([98]Figure 5C), suggesting that HFCF diet feeding
   promotes ceramide accumulation in the abdominal aorta of pigs. Ceramide
   synthesis in the abdominal aorta tissue was validated via qRT-PCR
   analysis, which confirmed significantly increased mRNA levels of CERS6,
   SGPP1, ASAH1, and SGMS1 in the HFCF group (p < 0.001) ([99]Figure
   5D–G).

   The above-mentioned results consistently revealed significant
   enrichment of apoptosis and the NF-kappa B signaling pathway. Several
   key genes associated with the NF-kappa B signaling pathway, including
   toll-like receptor 2 (TLR2), TLR4, inhibitor of nuclear factor kappa B
   kinase subunit epsilon (IKBKE), CC cytokine ligand-4 (CCL4), C-X-C
   motif chemokine ligand 10 (CXCL10), interferon regulatory factor 7
   (IRF7), and tumor necrosis factor-associated factor 1 (TRAF1),
   exhibited increased expression in the abdominal aorta tissue of the
   HFCF group ([100]Figure 5H). Genes related to apoptosis, such as
   cathepsin D (CTSD), Bcl2-associated agonist of cell death (BAD), tumor
   necrosis factor (TNF), and Caspase-8 (CASP8), were also upregulated in
   the abdominal aorta tissue of the HFCF group ([101]Figure 5I). TUNEL
   staining and NF-қB immunohistochemical staining confirmed increased
   apoptosis (p < 0.05) and NF-қB protein expression (p < 0.001) in the
   abdominal aorta tissue of the HFCF group, consistent with the RNA-Seq
   results ([102]Figure 5J–M).

3.4. Significantly Increased Iron Metabolism-Related Genes Were Observed in
the Aorta of Atherosclerotic Pigs Induced by High-Fat, High-Cholesterol, and
High-Fructose Diet

   Prior studies have highlighted iron accumulation in human
   atherosclerotic plaque areas and its close relationship to
   atherosclerosis [[103]16]. GO, KEGG, and GSEA analyses revealed
   dysregulation of the expression of iron absorption and transport genes
   in the abdominal aorta of the HFCF group of pigs. Notably, we observed
   a significant increase in the expression of transferrin receptor
   (TFRC), which is primarily responsible for iron uptake, and iron
   transporter (SLC40A1), which is primarily responsible for iron export,
   in the abdominal aorta of the HFCF group. Other genes involved in iron
   homeostasis, such as ferritin light chain (FTL) and nuclear receptor
   coactivator 4 (NCOA4), were also significantly upregulated following
   the HFCF diet ([104]Figure 6A). We corroborated the RNA-Seq results via
   qRT-PCR, demonstrating that TFRC (p < 0.0001), SLC40A1 (p < 0.0001),
   NCOA4 (p < 0.01), and FTL (p < 0.01) were upregulated in the HFCF group
   ([105]Figure 6B). Immunohistochemistry results indicated that TFRC and
   FTL protein expression significantly increased ([106]Figure 6C).
   Moreover, a significantly higher iron content was found in the
   abdominal aorta of the HFCF group compared with the NC group,
   corroborated by Perls’ iron staining ([107]Figure 6D, E). Taken
   together, these results suggest that HFCF diet feeding leads to iron
   overload due to the dysregulation of iron metabolism in the abdominal
   aorta of pigs.

Figure 6.

   [108]Figure 6
   [109]Open in a new tab

   Iron deposition in the aorta of atherosclerotic pigs induced by a
   high-fat, high-cholesterol, and high-fructose diet. (A) Heatmap showing
   the differentially expressed genes related to iron accumulation; (B)
   qRT-PCR analysis of TFRC, FTL, SLC40A1, and NCOA4 (n = 5); (C)
   representative photomicrographs and quantifications of TFRC and FTL
   staining in sections of the abdominal aorta in the NC and HFCF groups
   of pigs (n = 3); scale bars, 100 μm; (D) representative
   photomicrographs and quantifications of DAB-enhanced Perls’ iron
   staining of the aorta in the NC and HFCF groups of pigs (n = 3–4);
   scale bars, 250 μm; and (E) quantification of aortic iron
   concentrations (n = 5). NC, normal chow; HFCF, hig-fat,
   high-cholesterol, and high−fructose diet. All data were assessed using
   Student’s t-test and are presented as mean ± SEM. * p < 0.05, ** p <
   0.01, **** p < 0.0001.

3.5. C16 Cer Increases ROS Production, Apoptosis, and Inflammatory Pathway
Activation in Macrophages by Inducing Iron Overload

   While ceramide has been shown to induce apoptosis and inflammation
   [[110]17], little is currently known about whether it can induce these
   biological processes by affecting iron deposition, particularly in
   macrophages. By analyzing human and mouse aortic single-cell RNA-Seq
   data, we observed that key genes related to ceramide generation and
   iron deposition were highly expressed in macrophages
   ([111]Supplementary Materials, S1: Figure S4). Therefore, we
   investigated whether ceramide affects macrophage iron metabolism,
   subsequently impacting oxidative stress, apoptosis, and inflammation.
   RAW264.7 cells were treated with C16 Cer at concentrations ranging from
   0 to 200 μM for 24 h ([112]Figure 7A), and 100 μM C16 Cer was selected
   for the subsequent experiments. DCFH-DA probe results and flow
   cytometry revealed increased levels of ROS (p < 0.001) and apoptosis (p
   < 0.0001) in RAW264.7 cells after C16 Cer treatment ([113]Figure 7B–E).
   Furthermore, C16 Cer treatment downregulated the protein expression of
   BCL-2 in RAW264.7 cells (p < 0.01). Notably, it significantly
   upregulated the protein expression levels of Caspase-8 (p < 0.01) and
   Caspase-9 (p < 0.05). Additionally, protein expression levels of Bax
   and Caspase-3 increased after C16 Cer treatment, although the
   difference was not statistically significant. Moreover, after the C16
   Cer treatment, there was a significant increase in the protein
   expression levels of TLR4 (p < 0.001) and p-p65 NF-қ B (p < 0.05) in
   RAW264.7 cells ([114]Figure 7F–I).

Figure 7.

   [115]Figure 7
   [116]Open in a new tab

   C16 ceramide induces oxidative stress, apoptosis, and inflammatory
   response in macrophages. (A) Cell viability of RAW264.7 cells treated
   with 1–200 μM of C16 Cer for 24 h (n = 6); (B) ROS generation with C16
   Cer treatment was analyzed via 2′,7′-dichlorodihydro-fluorescein
   diacetate (DCFH-DA) staining; (C) the quantification of ROS levels in
   RAW264.7 cells (n = 3); (D) the apoptosis of RAW264.7 cells after C16
   Cer treatment was assessed via flow cytometric analysis; (E) the
   relative levels of apoptosis in RAW264.7 cells (n = 3); (F) immunoblot
   analysis of Bcl2, Bax, Caspase-3, Caspase-8, and Caspase-9 in RAW264.7
   cells; (G) the normalized band intensities of Bcl2, Bax, Caspase-3,
   Caspase-8, and Caspase-9 compared with β-actin (n = 3); (H) immunoblot
   analysis of TLR2, TLR4, p-p65 NF-қB, p65 NF-қB, p-p38 MAPK, and p38
   MAPK; (I) the normalized band intensities of TLR2, TLR4, p-p65 NF-қB,
   p65 NF-қB, p-p38 MAPK, and p38 MAPK compared with β-actin (n = 3). C16
   Cer, C16 ceramide. All data were assessed using Student’s t-test and
   are presented as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001,
   **** p < 0.0001.

   To confirm the role of iron in C16 ceramide-induced oxidative stress,
   apoptosis, and inflammation in macrophages, we pretreated RAW264.7
   cells with 50 μM of DFO. The results showed that the C16 Cer treatment
   significantly increased intracellular iron content (p < 0.05) and
   upregulated the protein expression levels of TFRC (p < 0.001) in
   RAW264.7 cells ([117]Figure 8A–C). Pretreatment with DFO mitigated the
   increase in TFRC (p < 0.01), intracellular iron content (p < 0.05), and
   ROS levels (p < 0.01) induced by C16 Cer treatment ([118]Figure 8A–E).
   Additionally, DFO pretreatment ameliorated the increased protein
   expression levels of TLR4 (p = 0.051) and Caspase-8 (p < 0.05) induced
   by C16 Cer treatment ([119]Figure 8A,B).

Figure 8.

   [120]Figure 8
   [121]Open in a new tab

   Deferoxamine reverses C16 Cer-induced oxidative stress, apoptosis, and
   inflammatory response in macrophages. (A) Immunoblot analysis of TFRC,
   Caspase-8, and TLR4; (B) the normalized band intensities of TFRC,
   Caspase-8 and TLR4 compared with β-actin (n = 3); (C) quantification of
   iron concentrations in RAW264.7 cells (n = 5); (D) the quantification
   of ROS levels in RAW264.7 cells (n = 3); and (E) ROS generation with
   C16 Cer and DFO treatment was analyzed via DCFH-DA staining. C16 Cer,
   C16 ceramide; DFO, deferoxamine. Data are expressed as mean ± SEM.
   One-way ANOVA analysis was performed followed by Tukey’s post hoc test.
   * p < 0.05, ** p < 0.01, *** p < 0.001.

4. Discussion

   Over the years, pigs have been widely utilized in cardiovascular
   disease research to study pathological mechanisms and therapeutic drug
   development for conditions like atherosclerosis. However, there is a
   dearth of studies investigating the characteristics and biological
   implications of lipid composition in atherosclerotic plaques in pigs.
   In this study, we employed LC-MS/MS-based lipidomics and RNA-Seq to
   expand our understanding of how lipid metabolism is altered in the
   abdominal aorta tissue of atherosclerotic pigs induced by an HFCF diet.
   Our results uncovered significant changes in lipid species composition
   in the aortas of atherosclerotic pigs, particularly sphingolipids,
   influenced by the length of acyl chains associated with sphingolipids
   as affected by the HFCF diet. Furthermore, the HFCF diet altered the
   aortic transcriptional profile, with DEGs enriched in pathways
   associated with sphingolipid metabolism, cellular iron ion homeostasis,
   apoptosis, and inflammatory responses. Interestingly, our findings
   suggested that C16 ceramide could be a key player in promoting iron
   deposition in macrophages, subsequently triggering increased
   intracellular ROS production, apoptosis, and the activation of the
   TLR4/NF-қB inflammatory pathway. These effects may play a crucial role
   in the pathogenesis of atherosclerosis in pigs induced by the HFCF
   diet.

4.1. Lipidomics Profiling of Atherosclerotic Plaques from Pigs and Other
Mammals

   It is widely acknowledged that oxidized low-density lipoprotein
   (ox-LDL), resulting from the oxidative modification of plasma-derived
   LDL, plays a significant role in the development of atherosclerosis. In
   addition to LDL, human atherosclerotic plaques have been found to
   contain various other lipid types, including sphingolipids and
   glycerophosphates. In our study, untargeted lipidomics revealed
   selective remodeling of several lipid classes in the abdominal aorta
   tissue of pigs subjected to an HFCF diet, including Cer, CerG1, CerG3,
   AcCa, TG, and LPC. Notably, previous studies have reported variations
   in the types and quantities of lipid species detected due to
   differences in detection conditions and sample sources. For example,
   Stegemann et al. [[122]18] identified 150 lipid species across nine
   different classes in human carotid endarterectomy specimens using a
   chip-based robotic nanoelectrospray platform interfaced with a triple
   quadrupole mass spectrometer (QqQ-MS). In their study, radial arteries
   were used as control samples, and the identified lipids included
   cholesterol ester (CE), SM, LPC, and PC. In another study, Edsfeldt et
   al. [[123]17] utilized high-performance liquid chromatography coupled
   with tandem mass spectrometry (HPLC-MS/MS) to analyze sphingolipids in
   human carotid plaques. They found significant increases in Cer,
   dihydroceramide (dhCer), glucosylceramide (GlcCer), lactosylceramide
   (LacCer), SM, and sphingosine-1-phosphate (S1P) in symptomatic plaques
   compared with asymptomatic ones. Additionally, Jung et al. [[124]19]
   employed ultra-performance liquid chromatography–quadrupole
   time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize the
   global metabolic profile of human aortic tissue samples containing
   plaques. Their findings showed elevated levels of sphingolipids (Cer,
   SM, and glucosylceramide) and glycerophospholipids (LPC, LPE, and PC)
   in aortas with plaques compared with control samples without plaques.
   Numerous lipidomics studies have been conducted in mouse models of
   atherosclerosis, with many focusing on peripheral blood samples
   [[125]20,[126]21,[127]22]. Notably, a recent study utilized LC-MS/MS to
   assess alterations in the lipid profile of thoracic aorta samples from
   APOE^−/− mice. A total of 131 differential lipids were identified in
   the samples, including TG (n = 53), PE (n = 24), PC (n = 10), PS (n =
   10), and SM (n = 7) [[128]23]. They found that the levels of all lipids
   in the PC and LPC classes were significantly increased in the high-fat
   diet group. However, the results of this study did not align with
   previous findings in APOE^−/− mice, with some discrepancies observed,
   such as no significant changes in Cer levels. In contrast, Kobayashi et
   al. [[129]24] discovered higher levels of Cer classes in the aortas of
   APOE^−/− mice compared with wild-type mice, including C16:0 Cer, C18:0
   Cer, and C24:0 Cer. It has been reported that there is a significant
   accumulation of ceramides d18:0 and d18:1 in the aorta of PCSK9^−/−
   mice fed a chow diet. When fed a Western diet, the accumulated
   ceramides in the aorta were higher in LDLR^−/− mice than those in
   PCSK9^−/− mice [[130]25]. Cao et al. [[131]26] identified 33
   plaque-specific lipids in high-fat diet-fed LDLR^−/− mice with mass
   spectrometry imaging, including one SM, three LPAs, four LPCs, two
   LPEs, and one LPI, and found that LPI 18:0 was predominantly localized
   in the necrotic core of the plaque. Similarly, Martin-Lorenzo et al.
   [[132]27] also found increased lysoPI (20:3) and SM (d18:0/15:0) in the
   aortic region of high-fat diet-fed LDLR^−/− mice with mass spectrometry
   imaging. Further studies will need to be performed to investigate the
   lipid profile in the aorta of patients or atherosclerotic animal models
   to provide an in-depth understanding of the complex pathogenesis of
   atherosclerosis.

4.2. Changes in PC and LPC in Pig Atherosclerotic Plaques

   In the present study, we observed significantly elevated levels of
   seven LPCs in pigs subjected to the HFCF diet compared with the NC
   group, including LPC (16:0e), LPC (16:1), LPC (18:0), LPC (18:0e), LPC
   (18:1), and LPC (18:2). In cells, LPC and PC can be interconverted,
   with PC being cleaved by phospholipase A2 (PLA2) to form LPC, while LPC
   can be converted back to PC by lysophosphatidylcholine acyltransferase
   (LPCAT). Furthermore, we noted alterations in various PC species, with
   increased PC (16:0/18:1) and PC (16:0/18:2), and decreased PC
   (18:0p/18:1) and PC (18:0/20:4). Similar results have been reported in
   studies on diseased iliac arteries from pigs with induced diabetes and
   hypercholesterolemia, indicating increased arterial LPC content and
   changes in PC composition, such as LPC (16:0), LPC (18:0), LPC (18:1),
   LPC (18:2), PC (16:0/18:2), and PC (18:1/18:2) [[133]28]. Additionally,
   our transcriptome data revealed significantly higher mRNA expression
   levels of lysophosphatidylcholine acyltransferase 2 (LPCAT2),
   phospholipase A2 group IVA (PLA2G4A), and group VII (PLA2G7) in the
   HFCF group of pigs compared with the NC group, consistent with the
   study by De Keyzer et al. [[134]29], who observed higher expression of
   PLA2G7 in monocytes and plaque macrophages of miniature pigs fed a
   cholesterol-rich diet when compared with controls.

4.3. Changes in Gene Expression Related to Sphingolipid Metabolism in Pig
Atherosclerotic Plaques

   It is well established that the ceramide synthesis pathway is complex,
   involving multiple processes. It can be generated from plasma membrane
   sphingomyelin by activating sphingomyelinases or synthesized de novo.
   Additionally, ceramide can be converted into sphingomyelin by
   sphingomyelin synthase or further metabolized through ceramidases. In
   our study, transcriptomic analysis revealed increased expression of
   genes responsible for ceramide biosynthesis (CERS6, SGPP1, and GALC)
   and utilization (ASAH1 and SGMS1). Research has shown that mammalian
   ceramide synthases play a role in the de novo generation of ceramides
   with specific fatty acid chain lengths. CERS6, in particular, is
   responsible for the preferential generation of C16 ceramide and has
   been implicated in atherosclerosis regulation [[135]30]. In our
   analysis of the aorta transcriptome of APOE^−/− mice induced by a
   Western diet, we found an upregulation of mRNA expression of CERS6,
   consistent with the increase in C16 Cer in the aortas of HFCF-fed pigs.
   We also observed an upregulation of SGPP1, which has been reported to
   catalyze the degradation of sphingosine-1-phosphate (S1P) through
   salvage and recycling of sphingosine into long-chain ceramides, having
   a pro-apoptotic effect by increasing ceramide expression [[136]31].
   Additionally, we noted increased expression of ASAH1 and GALC, two
   lysosomal enzymes—the former cleaving ceramide into sphingosine and
   free fatty acid, and the latter responsible for degrading
   galactosylceramide and sphingolipids to produce ceramide
   [[137]32,[138]33]. SGMS1, a member of the sphingomyelin synthase
   family, is involved in ceramide metabolism [[139]34]. Previous studies
   have indicated elevated expression of ASAH1 in human atherosclerotic
   plaques [[140]32]. Furthermore, CERS6, ASAH1, SGPP1, and SGMS1 are
   significantly enriched in M1-type macrophages within atherosclerotic
   plaques [[141]35]. These findings suggest a disruption in the balance
   of ceramide synthesis and degradation in atherosclerotic pig plaques
   induced by the HFCF diet, potentially leading to transformations
   between different sphingolipids. Edsfeldt et al. [[142]17] consistently
   sought to detect the gene expression profile of human atherosclerotic
   plaque but found no changes in the expression of genes related to
   sphingolipid metabolism. They speculated that this might be due to the
   activity of enzymes, especially considering that serum lipid levels in
   atherosclerosis patients were not significantly increased.

4.4. Dysregulation of Ceramide Metabolism Is Linked to Iron Deposition and
Activation of Related Pathways in Macrophages

   Ceramide reportedly induces apoptosis and proinflammatory responses in
   various cell types, particularly in murine and human macrophages, which
   are significant sites for iron storage. An overload of intracellular
   iron ions leads to the generation of ROS, resulting in apoptosis and
   inflammatory responses. In our study, we observed the induction of
   genes related to iron storage in atherosclerotic pig plaques induced by
   the HFCF diet, including TFRC, SLC40A1, and FTL. Treatment with
   long-chain C16 Cer induced TFRC protein expression and iron deposition
   in RAW264.7 cells. Moreover, it led to ROS production, apoptosis, and
   the expression of proteins associated with the TLR4/NF-қB pathway in
   these cells. Importantly, these changes could be reversed by
   pretreatment with deferoxamine. Our findings suggest a unique
   relationship between ceramide and iron in mediating cytotoxicity. In
   human HepG2 cells, ceramide accumulation promoted hepcidin expression,
   subsequently increasing intracellular iron content and triggering
   ceramide production [[143]36]. A previous study by Matsunage et al.
   [[144]37] demonstrated that C2 Ceramide induced oxidative damage and
   apoptosis in bovine aortic endothelial cells (BAECs), which DFO could
   reverse. These findings are in agreement with our results, indicating
   that ceramide may promote the development of atherosclerosis induced by
   the HFCF diet by affecting the iron ion balance, subsequently inducing
   macrophage apoptosis and activating inflammatory pathways. Targeted
   regulation of ceramide and iron metabolism in macrophages may hold
   potential clinical value for treating atherosclerosis induced by a
   high-fructose diet.

4.5. Therapeutic Potential of Ceramide

   Ceramides are implicated in a variety of pathological processes
   associated with cardiovascular disease, including inflammation,
   oxidative stress, and apoptosis. Studies have shown that the
   administration of ceramide analogs elevates ROS production in
   endothelial cells, perhaps owing to NADPH oxidase activation [[145]38].
   Studies have also confirmed that ceramide elicited an inflammation
   response in macrophages [[146]39]. In addition, the findings from
   studies of animal models suggest that administration of the serine
   palmitoyltransferase inhibitor myriocin decreased atherosclerotic
   lesion size in the aorta of APOE^−/− mice [[147]40,[148]41]. All these
   observations support a potential therapeutic role for ceramides in the
   treatment of atherosclerosis. However, ceramides are indispensable for
   a variety of biological processes, especially in the nervous and immune
   systems. Global inhibition of ceramide production is likely not the
   most feasible approach for treating atherosclerotic cardiovascular
   disease [[149]42]. In this study, we found that the enzymes responsible
   for ceramide synthesis were induced in the aorta of atherosclerotic
   pigs, leading to excessive accumulation of ceramide. Thus, targeting
   local ceramide production in the vascular tissue to reduce iron
   deposition and related processes may be more effective.

4.6. Limitations of this Study

   Our study has certain limitations. First, we solely verified the
   expression levels of sphingolipid metabolism genes obtained through RNA
   sequencing and did not measure their protein or enzyme activity due to
   a lack of suitable antibodies and reagents for pigs. Second, practical
   challenges in pig experiments prevented us from directly confirming the
   relationship between ceramide, apoptosis, and the activation of the
   TLR4/NF-қB inflammatory pathway in vivo. However, additional in vitro
   experiments provided compelling evidence that long-chain ceramide plays
   a crucial role in inducing cellular apoptosis, oxidative stress, and
   inflammation. Lastly, while we used LC-MS/MS technology to detect
   nearly 800 lipid species in atherosclerotic pig plaques, some lipid
   species, including CE, FA, and sphingosine, were identified in small
   abundance, suggesting that new lipidomics detection technology may be
   required for further analysis in the future.

5. Conclusions

   Overall, our lipidomics data unveiled alterations in the lipid
   composition of abdominal aorta tissues in HFCF diet-induced
   atherosclerotic pigs. We identified abnormal ceramide accumulation in
   atherosclerotic pig plaques, particularly long-chain C16 Cer.
   Subsequent analysis showed that C16 Cer induces the production of
   reactive oxygen species in an iron-dependent manner, leading to
   apoptosis and the activation of the TLR4/NF-қB pathway in macrophages
   ([150]Figure 9). Transcriptome results confirmed significant changes in
   gene expression related to sphingolipid metabolism, iron ion balance,
   apoptosis, and the TLR4/NF-қB pathway. Therefore, targeting C16
   ceramide and iron metabolism in macrophages may represent a novel and
   effective therapeutic approach against atherosclerosis.

Figure 9.

   [151]Figure 9
   [152]Open in a new tab

   Schematic diagram of the proposed pathological mechanism by which C16
   Cer increases ROS production, apoptosis, and inflammation response in
   macrophages and atherosclerosis by inducing iron overload. C16 Cer, C16
   ceramide; CERS6, ceramide synthase 6; TFRC, transferrin receptor; FTL:
   ferritin light chain; SLC40A1, iron transporter; TLR4, toll-like
   receptor 4; NF-қB, nuclear factor-kappa B; and ROS, reactive oxygen
   species.

Supplementary Materials

   The following supporting information can be downloaded at:
   [153]https://www.mdpi.com/article/10.3390/antiox13010004/s1,
   Supplementary file S1: Table S1. Information on primers used to perform
   qRT-PCR; Table S2. The abbreviations of detected lipid classes; Figure
   S1. Multivariate data analysis of LC-MS/MS lipidomics data from
   abdominal aorta samples from NC and HFCF pigs; Figure S2. Altered fatty
   acid composition of sphingolipids in the aorta of miniature pigs fed a
   HFCF diet; Figure S3. Gene set enrichment analysis showing significant
   enrichment in iron uptake and transport, toll-like receptor cascades,
   and apoptosis from NC and HFCF pigs; Figure S4. Ceramide generation and
   iron deposition related genes are found in macrophage cells in both
   human and mouse aortas. Supplementary file S2: Table S3. The detected
   lipid classes; Table S4. Lists of differentially expressed genes (DEGs)
   between NC and HFCF groups of pigs; Table S5. KEGG pathway analysis of
   differentially expressed genes (DEGs) between NC and HFCF groups of
   pigs; Table S6. GO enrichment analysis of differentially expressed
   genes (DEGs) between NC and HFCF groups of pigs.
   [154]Click here for additional data file.^ (3.3MB, zip)

Author Contributions

   Conceived and designed the experiments: Z.C. and M.C. Performed the
   experiments: Z.C., L.D., Y.F., Y.L., J.T., Y.C. and X.X. Analyzed the
   data: L.D., Y.F. and Y.R. Contributed reagents/materials/analysis
   tools: M.C. and Z.C. Writing-review and editing: Z.C., L.D., Y.F.,
   Y.L., J.T., Y.C., X.X. and M.C. All authors have read and agreed to the
   published version of the manuscript.

Institutional Review Board Statement

   The animal study protocol was approved by the Institutional Animal Care
   and Use Committee of the Zhejiang Chinese Medical University (Hangzhou,
   China, Approval No.: ZSLL-2016-0013).

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The data supporting the findings of this study are included in the
   article and [155]supplemental material. Raw RNA-Seq data are available
   in the Gene Expression Omnibus database ([156]GSE245530).

Conflicts of Interest

   The authors declare no conflict of interest.

Funding Statement

   This research was funded by grants from the National Natural Science
   Foundation of China (No.31970514), the Zhejiang Provincial Natural
   Science Foundation of China (No. LGD22C040008 and LGD20C040004), the
   Key R&D projects in Zhejiang Province (No. 2019C03031) and the Research
   Project of Zhejiang Chinese Medical University (No. 2023GJYY24).

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References