Abstract

   PM[2.5] can cause adverse health effects via several pathways, such as
   inducing pulmonary and systemic inflammation, penetration into
   circulation, and activation of the autonomic nervous system. In
   particular, the impact of PM[2.5] exposure on the liver, which plays an
   important role in metabolism and detoxification to maintain internal
   environment homeostasis, is getting more attention in recent years. In
   the present study, C57BL/6J mice were randomly assigned and treated
   with PM[2.5] suspension and PBS solution for 8 weeks. Then, hepatic
   tissue was prepared and identified by metabolomics analysis and
   transcriptomics analysis. PM[2.5] exposure can cause extensive
   metabolic disturbances, particularly in lipid and amino acids metabolic
   dysregulation.128 differential expression metabolites (DEMs) and 502
   differently expressed genes (DEGs) between the PM[2.5] exposure group
   and control group were detected. The Kyoto Encyclopedia of Genes and
   Genomes (KEGG) enrichment analyses showed that DEGs were significantly
   enriched in two disease pathways, non-alcoholic fatty liver disease
   (NAFLD) and type II diabetes mellitus (T2DM), and three signaling
   pathways, which are TGF-beta signaling, AMPK signaling, and mTOR
   signaling. Besides, further detection of acylcarnitine levels revealed
   accumulation in liver tissue, which caused restricted lipid
   consumption. Furthermore, lipid droplet accumulation in the liver was
   confirmed by Oil Red O staining, suggesting hepatic steatosis.
   Moreover, the aberrant expression of three key transcription factors
   revealed the potential regulatory effects in lipid metabolic disorders,
   the peroxisomal proliferative agent-activated receptors (PPARs)
   including PPARα and PPARγ is inhibited, and the activated sterol
   regulator-binding protein 1 (SREBP1) is overexpressed. Our results
   provide a novel molecular and genetic basis for a better understanding
   of the mechanisms of PM[2.5] exposure-induced hepatic metabolic
   diseases, especially in lipid metabolism.

   Keywords: particulate matter, metabolomics, transcriptomics, hepatic
   steatosis, PPARα, PPARγ, SREBP1

1. Introduction

   With the rapid development of the world population, economy, and
   industrialization, energy consumption also grows increasingly. Air
   pollution has become an urgent global health problem ([39]1, [40]2).
   Particulate matter 2.5 (particulate matter with an aerodynamic diameter
   of ≤ 2.5 µm, PM[2.5]), as one of the main pollutants, can penetrate the
   respiratory barrier simply with its extremely tiny particle diameter by
   absorbing and combining with toxic compounds ([41]3, [42]4). Then it
   can reach almost all organs through the bloodstream ([43]5). Many
   epidemiological, animal, and in vitro studies found that health effects
   can be caused by exposure to airborne PM[2.5], such as increased
   morbidity and mortality rates, even at concentrations meeting the
   environmental criteria ([44]6–[45]8). PM[2.5] can lead to extensive
   inflammation ([46]9) and irreversible damage in almost all systems
   ([47]10, [48]11), which causes respiratory diseases like asthma and
   lung cancer ([49]12, [50]13), circulatory diseases like ventricular
   hypertrophy and heart disease ([51]14), and neurological diseases like
   retinopathy ([52]15, [53]16).

   As an important metabolic center of the body, the liver plays an
   important role in maintaining the homeostasis of the internal
   environment and energy system ([54]17). It participates in the
   regulations of the synthesis, storage, decomposition, detoxification,
   transformation, and excretion of xenobiotics in the organism ([55]18,
   [56]19). The liver is critical in regulating lipid metabolism, amino
   acid metabolism, steroid metabolism, and many other metabolic pathways
   ([57]20), of which metabolic disorders can result in severe damage and
   the promotion of function disturbances, for example, nonalcoholic fatty
   liver disease (NAFLD) ([58]21). NAFLD covers the hepatic pathological
   change progression from steatosis to nonalcoholic steatohepatitis,
   fibrosis, and cirrhosis ([59]20, [60]22–[61]24), and can notably
   increase the risk of some more metabolic diseases such as diabetic
   cardiovascular disease and cancer ([62]17, [63]24).

   Previous research indicated that PM[2.5] exposure affects various
   metabolic pathways in vivo ([64]25), especially the pathways associated
   with lipid metabolism ([65]18). Recent studies have shown that the
   liver has more obvious responses than the lung in metabolic disorders
   induced by PM[2.5] exposure ([66]26). PM[2.5] exposure possibly
   contributes to dyslipidemia, vascular inflammation, lipid dysfunction,
   and insulin resistance, and then accelerates lipid-associated metabolic
   diseases such as atherosclerosis and type II diabetes mellitus (T2DM)
   ([67]14, [68]27, [69]28). The impact of PM[2.5] exposure on the liver
   has been getting more attention these years. Meanwhile, epidemiological
   studies suggested that airborne PM[2.5] can improve the liver enzyme
   level ([70]29), cause chronic liver inflammation, and increase the risk
   of cirrhosis and liver cancer ([71]11, [72]30, [73]31). PM[2.5]
   exposure has also been identified as an independent risk factor for
   NAFLD ([74]21, [75]23). Previous toxicologic studies have shown that
   PM[2.5] can transfer from the lung to the liver through multiple routes
   ([76]26, [77]32), and it may expedite the progression of NAFLD through
   mechanisms such as inflammatory responses, oxidative stress, and
   insulin resistance ([78]33, [79]34). PM[2.5] exposure can induce
   insulin resistance (IR) via endothelial dysfunction, affecting hepatic
   insulin signaling pathways and suppressing the expression of peroxisome
   proliferator-activated receptors gamma (PPARγ) and PPARα, resulting in
   hepatic lipid accumulation ([80]35, [81]36). Concurrently, PM[2.5]
   exposure can cause oxidative stress, leading to hepatic tissue damage
   ([82]37, [83]38). Moreover, exposure to PM[2.5] may promote the
   expression of proinflammatory cytokines in adipocytesto and cause
   inflammation in NASH ([84]39, [85]40).

   This study randomly assigned C57BL/6J mice into two groups and treated
   them with PM[2.5] suspension and PBS solution for eight weeks. The
   differences in liver tissue between the two groups, especially in
   metabolism and gene expression, were compared to investigate the impact
   of PM[2.5] exposure on liver metabolism. The histopathological changes,
   differential expression of genes, and metabolites related to hepatic
   metabolic disorders were also analyzed to elaborate on the basic and
   comprehensive condition of the liver, illustrating the mechanism of
   injury triggered by PM[2.5] exposure. To some extent, insights into
   hepatic metabolic disturbances benefit understanding the molecular
   pathology of metabolic diseases and exploring the potential therapeutic
   target.

2. Materials and methods

2.1. PM[2.5] preparation and reagents

   PM[2.5] samples were collected and prepared according to previous
   studies ([86]41), collected at Shanghai Jiao Tong University School of
   Medicine, Chongqing Road (S), Shanghai, China, from December 2017 to
   January 2018. The sampling site was close to the main traffic artery,
   and the surrounding area was densely populated. PM[2.5] samples were
   collected on glass fiber filters for biological assay using a
   high-volume sampler (#2031, Lonying Company, Shandong, China) or quartz
   filters for chemical assay using a high-flow cascade sampler (#HFI 131,
   MSP Company, MN, USA) for 96 h. The collected PM[2.5] sampling film was
   cut to the same size (3 cm × 3 cm) and randomly mixed. PM[2.5] samples
   were split into four aliquots for chemical assay, which were analyzed
   for metal elements, polycyclic aromatic hydrocarbons (PAHs), elemental
   and organic carbon (EC and OC), and inorganic ion elements. The
   remaining sampling membranes were freeze-dried to separate PM[2.5] for
   later animal experiments.

   Metal elements in PM[2.5] (Ca, Fe, Na, K, Al, Mg, Zn, Mn, Pb, Cu, Ba,
   Ti, Si, Sb, V, Mo, Sn, Ni, As, W, Cd) were measured by inductively
   coupled plasma mass spectrometry (# iCAP Q, ThermoFisher, MA, USA).
   Also, 16 PAHs (Naphthalene, Alenene, Acenaphthene,
   Indeno[1,2,3-cd]Pyrene, Benzo[b]Fluorathene, Fluorene, Pyrene,
   Benzo[k]Fluorathene, Benzo[g,h,i]Perylene, Fluoranthene,
   Benzo[a]Pyrene, Chrysene, Dibenz[a,h]Anthracene, Phenanthrene,
   Benz(a)Anthracene, Anthracene) were analyzed by gas chromatography-mass
   spectrometry (#*/7890A-5975C, Agilent, CA, USA). The inorganic ion
   elements (Ca^2+, Na^+, K^+, Mg^2+, SO[4] ^2-, NO[2] ^-, NO[3] ^-, Cl^-,
   PO[4] ^3-) were analyzed by ion chromatography (#*ICS-5000+/900,
   Dionex, CA, USA). Thermophotometry detected the EC and OC content with
   a total organic carbon analyzer (#DRI Model 2015, DRI, NV, USA). The
   Instrumental Analysis Center of Shanghai Jiao Tong University provided
   the whole procedure of analysis and data. The results are shown in
   [87]Supplementary Table 1 .

   PM[2.5] samples were extracted for the biological assay by immersing
   the filters in ultrapure water and sonicating for 30 min (500 W, 40
   kHz). Then they were recollected by a vacuum freeze drier (FreeZone2.5,
   Labconco Company, MO, USA). The extracted PM[2.5] samples at 100 mg/mL
   concentration were stored at −80°C before animal and cell exposure.

2.2. Design of animal experiment

   C57BL/6J mice (6 weeks old, n=12), purchased from the Animal Center of
   the Southern Model (Shanghai, China), were fed at relative temperature
   and humidity with a normal diet and water, providing ad libitum
   throughout the experiment. A total of twelve male mice were randomly
   divided into two groups, regardless of gender. After 2-week
   acclimation, mice in the PM[2.5] group (n=6) were directly intranasally
   treated with 10 μL PM[2.5] suspension (100 μg PM[2.5]/day) for 8 weeks,
   while the control group (n=6) were administered 10 μL phosphate buffer
   saline (PBS), which was in correspondence with the previous studies
   ([88]38). When the exposure ended, all mice were subjected to a
   euthanasia procedure and then dissected. During the euthanasia
   procedure, we used the Rodent Anesthesia Machine (Gas Anesthesia, USA)
   with isoflurane as the anesthesia gas. We poured isoflurane into the
   anesthetic volatile tank, which was connected to the oxygen cylinder.
   Through the oxygen air blowing, volatile isoflurane was used with a
   concentration of 1.5-3%. The mice were anesthetized in 1-2 minutes and
   the body mass was measured. Then, liver samples were collected,
   weighed, and finally stored in a −80°C refrigerator for indicated
   experiments and analysis. Animal experiments have been approved by the
   animal and ethics review committee of the laboratory animal center at
   Shanghai Jiao Tong University School of Medicine (Shanghai, China).

2.3. Liver histological staining

   Frozen liver tissues were cut into sections (3-5 μm) with the frozen
   slice system. Part of the​sections were stained with hematoxylin and
   eosin (H&E) for histology analysis according to standard protocols
   (#E607318, Sangon Biotech (Shanghai) Co., Ltd.). The tissue sections
   were stained with lipid droplets with freshly prepared Oil-Red-O
   solution (#E607319, Sangon Biotech (Shanghai) Co., Ltd.) and nuclei
   with hematoxylin (#E607318, Sangon Biotech (Shanghai) Co., Ltd.) to
   assess the lipid accumulation in liver tissues. The specimens were
   observed and photographed randomly in six fields of view by
   fluorescence microscopy (Olympus, Japan). Meanwhile, quantitative
   analysis of the Oil Red O positive area was analyzed with Image-Pro
   Plus 6.0 software (Media Cybernetics, Inc., USA), expressed as a
   percent.

2.4. Determination of hepatic TG and FFA levels

   Hepatic TG and FFA levels were measured by the Triglyceride Detection
   Kit (#D799795, Sangon Biotech (Shanghai) Co., Ltd.), the Nonesterified
   Fatty Acid Detection Kit (#D799793, Sangon Biotech (Shanghai) Co.,
   Ltd.) according to the manufacturer’s instructions, respectively. A BCA
   Protein Test kit (#C503021) was used to calibrate TG and FFA levels.

2.5. Quantitative real-time PCR analysis

   Total RNA from frozen liver tissue samples was extracted by Trizol
   (Invitrogen, Carlsbad, CA, USA). The experiment was conducted in the
   same way as in our previous study ([89]38). The target gene studied was
   pparα, while the reference gene was β-actin. The corresponding primer
   pairs are designed from Primerbank
   ([90]https://pga.mgh.harvard.edu/primerbank/) as follows. pparα,
   forward primer: AGAGCCCCATCTGTCCTCTC; reverse primer:
   ACTGGTAGTCTGCAAAACCAAA; β-actin, forward primer:
   GGCTGTATTCCCCTCCATATATCG; reverse primer: CCAGTTGGTAACAATGCCATGT.

2.6. Protein level analysis

   For immunofluorescence assay, frozen liver tissue sections were
   prepared and performed for 30 min at normal temperature before
   commencing with the staining protocol. Then, the slices were washed
   with PBS three times and blocked with 5% Bovine Serum Albumin (BSA) for
   1 h. SREBP1 (#ab71983, 1:200, Abcam, USA) and PPARγ (#ab41928, 1:100,
   Abcam, USA) primary antibodies were incubated overnight. Slides were
   incubated with Anti-rabbit or anti-mouse IgG secondary antibody (#2975,
   #4408, 1:500, Cell Signaling Technology, USA) for 1 h, followed by
   nuclear staining with 4’,6-diamidino-2-phenylindole (DAPI) (#D9542,
   Sigma, USA) for 30 s. Mounted slides were observed using fluorescence
   microscopy (Olympus, Japan) to obtain the fluorescence images.

   Western Blot analysis was operated as described in our previous studies
   ([91]38). The primary antibodies included anti-SREBP1 (#ab71983,
   1:1000, Abcam, USA), anti-PPARγ (#ab41928, 1:1000, Abcam, USA), and
   anti-PPARα (#ab41928, 1:1000, Abcam, USA). As loading control, β-Actin
   (#ab8226, 1:2000, Abcam, USA) was used.

2.7. Quantitative detection of acylcarnitine in liver

   Acylcarnitine was profiled by liquid chromatography-tandem mass
   spectrometry, and the detailed method was described previously ([92]42,
   [93]43). In brief, a total of 66 acylcarnitine were measured, including
   free carnitine (C0), 11 short-chain acylcarnitine (C2, C3, C3:0-OH, C4,
   C5, C5:0-2, C5:0-OH, C5:1, C6, C6-DC, C7:0-DC), 24 medium-chain
   acylcarnitine (C8, C8:0-OH, C10, C10-DC, C10:0-DC-OH, C10:1, C10:2-OH,
   C10:3-1, C10:3-DC, C12, C12:0-DC, C12:0-OH, C12-OH, C14, C14:0-DC,
   C14:0-OH, C14:1, C14:1-2, C14:1-DC-2, C14:2, C15:0-2, C15:2, C15:2-DC,
   and C15:2-OH), and 30 long-chain acylcarnitine (C16, C16:0-DC, C16:1,
   C16:1-DC, C16:1-OH, C16:2, C16:2-OH, C17, C17:1, C17:1-DC, C17:1-DC-OH,
   C17:2-OH, C17:3, C18, C18:0-OH, C18:1-OH, C18:2-OH, C20, C20:0-OH,
   C20:1, C20:1-OH, C20:2, C20:2-DC, C20:2-OH-1, C20:2-OH-2, C20:3,
   C20:3-OH, and C20:4).

2.8. High-resolution mass spectrometric analysis of liver sample

   A total of eight mouse liver tissue samples were collected, and tissue
   preparation strictly followed the procedure described in the method
   METAB_NonTargeted_0001.00 (i.e., DMPA-labeling Kit for Amine &
   Phenol/Hydroxyl/Carboxyl Metabolomics I). The LC-MS analysis strictly
   followed the SOP (i.e., LC-MS Analysis for Dansyl-labeled
   Amine&Phenol/Hydroxyl/Carboxyl Metabolomics) using the HP-CIL
   Metabolomics Platform. Analysis was performed using IsoMS Pro 1.2.10
   (NovaMT Inc.) and NovaMT Metabolite Database v1.0. A total of 8 samples
   assigned to 2 groups were uploaded to IsoMS Pro 1.2.10. Data Quality
   Checks and Data Processing were performed. Data were cleaned with peak
   pairs where Mean (sample)/Mean (blank) was less than or equal to 4.0 at
   a significance level of 0.05 and were filtered out. Peak pairs without
   data present in at least 50.0% of all samples and 80.0% of samples in
   any group were filtered out-the Ratio of Total Useful Signal normalized
   data. Metabolite identification was performed using the CIL library and
   LI library.

   MetaboAnalyst 5.0 was used to analyze the differences between the
   control group and the PM[2.5] exposure group in endogenous metabolites
   of the liver and to construct principal component analysis (PCA) and
   partial least squares-discriminant analysis (PLS-DA) models.
   Identifying the perturbed biological pathways on the differential
   metabolite data was performed using the Kyoto Encyclopedia of Genes and
   Genomes ([94]https://www.kegg.jp, KEGG). Quantitative enrichment
   analysis (QEA) was performed using a generalized linear model to
   estimate a Q-statistic for each metabolite set, which describes the
   correlation between compound concentration profiles, X, and clinical
   outcomes, Y. All the pathways with adjusted p-value <0.05 were
   considered as the biological pathways perturbed by chronic exposure to
   PM[2.5]. Differential metabolites were defined according to the
   following criteria: FC > 1.5 or FC < 0.67 with a p-value < 0.05 and a
   q-value < 0.05.

2.9. High-throughput RNA sequencing

   Total RNA was isolated and purified using TRIzol reagent (Invitrogen,
   Carlsbad, CA, USA) following the manufacturer’s procedure. Each
   sample’s RNA amount and purity were quantified using NanoDrop ND-1000
   (NanoDrop, Wilmington, DE, USA). The RNA integrity was assessed by
   Bioanalyzer 2100 (Agilent, CA, USA) with RIN number >7.0 and confirmed
   by electrophoresis with denaturing agarose gel. Poly (A) RNA is
   purified from 1μg total RNA using Dynabeads Oligo (dT) 25-61005 (Thermo
   Fisher, CA, USA) using two rounds of purification. Then the poly (A)
   RNA was fragmented into small pieces using Magnesium RNA Fragmentation
   Module (NEB, cat.e6150, USA) for 94°C 5-7min. Then the cleaved RNA
   fragments were reverse-transcribed to create the cDNA by SuperScript™
   II Reverse Transcriptase (Invitrogen, cat. 1896649, USA), which were
   next used to synthesize U-labeled second-stranded DNAs with E. coli DNA
   polymerase I (NEB, cat.m0209, USA), RNase H (NEB, cat.m0297, USA) and
   dUTP Solution (Thermo Fisher, cat.R0133, USA). An A-base is then added
   to the blunt ends of each strand, preparing them for ligation to the
   indexed adapters. Each adapter contains a T-base overhang for ligating
   the adapter to the A-tailed fragmented DNA. Single- or dual-index
   adapters are ligated to the fragments, and size selection was performed
   with AMPureXP beads. After the heat-labile UDG enzyme (NEB, cat.m0280,
   USA) treatment of the U-labeled second-stranded DNAs, the ligated
   products are amplified with PCR by the following conditions: initial
   denaturation at 95°C for 3 min; 8 cycles of denaturation at 98°C for
   15s, annealing at 60°C for 15 sec, and extension at 72°C for 30 sec;
   and then final extension at 72°C for 5 min. The average insert size for
   the final cDNA library was 300 ± 50 bp. At last, we performed the 2×150
   bp paired-end sequencing (PE150) on an Illumina Novaseq™ 6000
   (LC-Biotechnology CO., Ltd., Hangzhou, China) following the vendor’s
   recommended protocol.

   Raw data were filtered to remove the reads with the connector
   (adaptor), reads containing more than 5% ambiguous nucleotides, and
   low-quality reads (mean Q-value <20) using Cutadapt before analysis.
   Statistics of the raw sequencing amount, the effective sequencing
   amount, Q20, Q30, GC content, and comprehensive evaluation were
   performed. Total RNA was isolated and sequenced by Lc-Bio Technologies
   Co., Ltd. (Hangzhou, China). The data of RNA sequencing (RNA-SEQ) is
   stored in csv format after conversion, and the analysis of significant
   differences between samples and the relative quantification of the
   transcripts is performed by means of the DESeq2 package in R. The
   p-values of 0.05 were set as the threshold for significantly
   differential expression. The screening criteria for differentially
   expressed genes were defined as genes with FC > 1.50 or FC < 0.67 with
   p-value < 0.05. Meanwhile, pathway enrichment analysis was performed
   for differentially expressed genes by KEGG and Gene Ontology (GO). KEGG
   enrichment analyses of differentially expressed genes were implemented,
   and p-value<0.05 were considered significantly enriched.

2.10. Data processing and statistical analysis

   The experimental data of acylcarnitine (ten samples in each group) were
   expressed as the Mean ± standard error of Mean (`Mean ± SEM) and
   analyzed with SPSS Statistics 26.0 software, using Student-t test
   (two-side). GraphPad Prism5 software was used for statistical analysis
   and bar plot across all staining counts data. Differences between the
   two groups were considered to be significant when the p-value < 0.05.
   The relationship between differential metabolites and differentially
   expressed genes. Cytoscape v3.9.0 was used to plot the network diagram
   for the selected parts with a p-value < 0.001. The correlation analysis
   between DEGs and DEMs was evaluated by the cor function in the R
   package and the network plot was performed with Cytoscape v3.9.0. The
   analysis of metabolomics and transcriptomics data, which was performed
   with four independent samples in each group (transcriptomics control
   group only with three groups) were generated with MetaboAnalyst 5.0 and
   OmicStudio ([95]https://www.omicstudio.cn), showing with the PCA plots,
   volcano plots, advanced heatmap plots, and enrichment plots. The
   significant enriched biological pathways performed for DEMs and DEGs
   were screened with relative counts number, enrich factors and p-value
   of the selected enriched pathways. Information in detail about
   OmicStudio was shown elsewhere ([96]44, [97]45).

3. Result

3.1. Chemical composition analysis of PM[2.5]

   To evaluate the physical and chemical composition of PM[2.5], metal
   elements, polycyclic aromatic hydrocarbons (PAHs), elemental carbon
   (EC) and organic carbon (OC), and inorganic ionic elements were
   detected. As illustrated in [98]Supplementary Table 1 , Calcium (9865
   ng/mg), Sodium (3794 ng/mg), Iron (2418 ng/mg), and Potassium (1362
   ng/mg) were the main elements in PM[2.5]. The content of Naphthalene
   was the highest and much higher than the other 14 PAHs, up to 6.73
   ng/mg. Acenaphthene, Pyrene, and Benzo[b]Fluorathene were 0.89 ng/mg,
   0.76 ng/mg, and 0.67 ng/mg, respectively. The cation and anion contents
   in PM[2.5] were mainly as followings: NH[4] ^+ (3992 ng/mg), Ca^2+
   (1189 ng/mg), K^+ (865 ng/mg), Mg^2+ (147 ng/mg), SO[4] ^2- (3767
   ng/mg), NO[3] ^- (2343 ng/mg), Cl^- (74 ng/mg), PO[4] ^3- (68 ng/mg).
   The content of organic carbon (OC) and elemental carbon (EC) were 641
   ng/mg and 177 ng/mg, respectively, as the ratio of OC/EC was 3.62.

3.2. Effects of PM[2.5] exposure on histopathological analysis

   After 8-week PM[2.5] exposure, there was no significant difference in
   body weight ([99] Figure 1A ) and liver weight ([100] Figure 1B )
   compared with the control group. To evaluate the effects of PM[2.5]
   exposure on the liver of mice, we assessed the histological changes. As
   shown in the H&E staining results ([101] Figure 1C ), obvious
   pathological changes in the liver, including visible hepatic steatosis,
   irregular hepatic cords arrangement, and partial cytoplasmic
   vacuolation, can be seen in the PM[2.5] exposure group, demonstrating
   the significant liver damage caused by PM[2.5].

Figure 1.

   Figure 1
   [102]Open in a new tab

   The Effect of PM[2.5] exposure on the body weight, liver weight, and
   hepatic histology in mice. (A) Body weight; (B) Liver weight; (C)
   Representative pathological changes revealed by H&E staining of liver
   tissue. (a) Control; (b) PM[2.5]. Scale bar, 100 µm. n.s not
   significant.

3.3. General analysis of hepatic metabolism profile after PM[2.5] exposure

   Then the metabolomics analysis of tissue was performed to investigate
   the effect of PM[2.5] exposure on the liver. The principal components
   analysis (PCA) was carried out to visualize variation in identified
   metabolites between the PM[2.5] exposure group and the control group
   ([103] Figure 2A ). The results showed that PC1 represents 52.51% of
   the variation, and 26.2% is represented by PC2. Correlation analysis
   was executed to test the relevance and repeatability between samples
   ([104] Figure 2B ). It can be seen that the PM[2.5] exposure group was
   separated from the control group, indicating that endogenous substances
   in the PM[2.5] exposure group have changed compared with the control
   group, manifested in some specific metabolites.

Figure 2.

   [105]Figure 2
   [106]Open in a new tab

   The Effect of PM[2.5] exposure on the hepatic metabolites in mice. (A)
   PCA score plot. (B) Correlation analysis of all the biological samples.
   (C) Volcano plot of significant metabolites. (D) Cluster heatmap of
   differential metabolites. (E) KEGG enrichment analysis of differential
   metabolites. The y-axis represents the enrichment pathway, while the
   x-axis represents the enrichment ratio. The dot sizes represent the
   metabolites number. The color represents the p-value.

   The total of 645 metabolites annotated by two groups are shown in the
   volcano plot ([107] Figure 2C ), which was constructed by plotting each
   metabolite’s fold change (FC) against the p-value. For comparison of
   the PM[2.5] exposure group and the control group, the analysis showed
   67 peak pairs with FC>1.5, p-value<0.05 (in red), and 61 peak pairs
   with FC<0.67, p-value< 0.05 (in blue). Hierarchical clustering analyses
   were used to elaborate on accumulating the 128 differential
   metabolites. [108]Figure 2E shows the part of the top 43 differential
   metabolites between the two groups (FC>2 or FC<0.5, p-value< 0.05), and
   a more detailed view is shown in [109]Supplementary Figure 1 .
   Differential metabolites between the two groups are mainly amino acids
   and derivatives, lipids and lipid-like molecules, organic acids and
   derivatives, and nucleosides, nucleotides, and nucleotide analogs. The
   metabolites that decreased most significantly under PM[2.5] exposure
   are 2-hydroxymuconate semialdehyde and 1,3,5-trihydroxy benzene.
   However, 1-Phenylethylamine, Glutaconic acid, Mesaconic acid, and
   Prostaglandin C2 increased remarkably ([110] Supplementary Figure 2 ).

   According to the KEGG analysis, there are a total of 37 KEGG pathways
   enriched in the PM[2.5] exposure group ([111] Supplementary Table 2 ).
   Moreover, the top 20 pathways include alanine, aspartate, glutamate
   metabolism, pyrimidine metabolism, vitamin B6 metabolism, arginine
   biosynthesis, arginine, proline, and purine metabolism etc, which are
   presented in [112]Figure 2D . All these significantly enriched pathways
   mainly gather on amino acid and fatty metabolism, followed by glucose
   and vitamin metabolism. Six pathways (butanoate metabolism, fatty acid
   biosynthesis, arachidonic acid metabolism, biosynthesis of unsaturated
   fatty acids, linoleic acid metabolism, and α-Linolenic acid metabolism)
   are associated with fatty metabolism. Furthermore, amino sugar and
   nucleotide sugar metabolism, galactose metabolism, and citrate cycle
   (TCA cycle) were three pathways related to glucose metabolism.

3.4. General analysis of hepatic gene expression profiles after PM[2.5]
exposure

   In addition to the metabolomics analysis, transcriptomics analysis was
   performed to provide an overview of changes in hepatic gene profile
   after PM[2.5] exposure and to explore the deeper effects of PM[2.5]
   exposure on the liver. Correlation analysis was executed to test the
   relevance and repeatability between samples ([113] Figure 3A ). The
   results showed that the control samples were reproducible, while
   PM[2.5]exposed samples had a high pairwise correlation.

Figure 3.

   [114]Figure 3
   [115]Open in a new tab

   The effect of PM[2.5] exposure on the hepatic gene expression in liver
   tissue. (A) Correlation analysis of all the biological samples. (B)
   Volcano plot showing the DEGs in the liver. KEGG (D) and GO (E)
   enrichment of class bar plot. The y-axis represents the enrichment of
   the subclass, while the x-axis represents a percent of genes. The bar’s
   color represents the different KEGG main classes (see color scale). (C)
   KEGG Enrichment scatter plot of DEGs. The y-axis represents the
   enrichment pathway, while the x-axis represents the rich factor. The
   dot sizes represent the gene number. The color represents the p-value.

   Initially, setting the threshold as FC>1.5 or FC<0.67 with a
   p-value<0.05, a total of 502 differently expressed genes (DEGs) (291
   upregulated DEGs and 211 downregulated DEGs) were found between the
   PM[2.5] exposure group and control group ([116] Figure 3B ).

   GO and KEGG analyses were performed to understand these DEGs’ functions
   better. It was found that 25 KEGG pathways ([117] Supplementary Table 3
   ) and 599 GO terms were significantly enriched, respectively. In
   [118]Figure 3E , the top enriched GO terms are in three subclasses
   including biological process, cellular component, and molecular
   function. Meanwhile, 25 KEGG significantly enriched pathways are mainly
   related to fatty and amino acid metabolism. It is worth noting that
   PM[2.5] exposure also significantly affected three signaling pathways:
   TGF-β signaling, AMPK signaling, and mTOR signaling. Moreover, the
   pathways of diseases related to metabolic disorders, such as NAFLD and
   T2DM, were also significantly enriched under PM[2.5] exposure. The top
   20 enriched pathways, according to the KEGG database, are shown in
   [119]Figure 3D . We also performed a taxonomic analysis to demonstrate
   the proportion of significantly expressed genes in these DEGs
   subclasses. Under Metabolism terms, these DEGs mainly reacted in five
   main subclasses, including Metabolism of cofactors and vitamins,
   Xenobiotics biodegradation and metabolism, Amino acid metabolism,
   Metabolism of other amino acids, and lipid metabolism ([120] Figure 3C
   ).

3.5. Effects of PM[2.5] exposure on hepatic levels of acylcarnitine

   Combining the metabolomics and transcriptomics data, we found that,
   though several different pathways are involved, pathways related to
   lipid metabolism were significantly highlighted in both the gene
   expression and metabolites in the PM[2.5] exposure group. Previous
   studies have proved that acylcarnitine, a kind of ester in carnitine,
   plays a key role in fatty acid oxidation. So, we measured the
   acylcarnitine levels in the liver tissue to observe the lipid metabolic
   level. The standardized quantitative detection result of acylcarnitine
   in the liver is presented in [121]Table 1 , presented in terms of mean
   ± standard error of the Mean, median, and minimum-maximum for each
   acylcarnitine profile of the PM[2.5] exposure group and the control
   group. Sixty-six kinds of acylcarnitine were detected in the two
   groups, and 23 carnitine content significantly changed among them.
   Comparing acylcarnitine levels in control and PM[2.5] exposure, groups
   revealed a significant decrease only in C14:1-2 (*p<0.05). Meanwhile, a
   significant increase was observed in average C0, C3:0-OH, C5:0-OH,
   C6-DC, C7:0-DC, C10:0-DC-OH, C10:2-OH, C10:3-DC, C14:0-DC, C15:2-DC,
   C16, C16:0-DC, C16:1, C16:1-DC, C17:1, C18, C18:1, C18:1-OH, C20:0,
   C20:0-OH, C20:2-OH-2, C20:3 levels (*p<0.05). There was no
   statistically significant difference in other acylcarnitine levels
   (p>0.05). The analysis of hepatic acylcarnitine levels showed that the
   lipid metabolism of the liver was disordered.

Table 1.

   Acylcarnitine values of liver tissue.
                      Control         PM[2.5]            p
                      Mean ± SEM      Mean ± SEM
                      Median(min-max) Median(min-max)
   C0 (free carnitin) 4.93 ± 0.29     7.61 ± 0.61      0.002
                      5.05(3.3-5.99)  8.5(4.23-9.32)
   C3:0-OH            0.24 ± 0.04     0.41 ± 0.04      0.01
                      0.23(0.06-0.42) 0.46(0.2-0.57)
   C5:0-OH            0.34 ± 0.02     0.46 ± 0.04      0.022
                      0.35(0.22-0.43) 0.41(0.34-0.74)
   C6-DC              1.34 ± 0.17     2.39 ± 0.26      0.003
                      1.16(0.71-2.3)  2.45(1.3-4.02)
   C7:0-DC            0.41 ± 0.04     1.06 ± 0.1       <0.001
                      0.38(0.28-0.63) 0.98(0.81-1.85)
   C10:0-DC-OH        0.02 ± 0        0.07 ± 0.01      <0.001
                      0.02(0.01-0.03) 0.07(0.04-0.15)
   C10:2-OH           0.03 ± 0.01     0.06 ± 0.01      0.012
                      0.03(0.01-0.07) 0.06(0.03-0.09)
   C10:3-DC           0.08 ± 0.01     0.22 ± 0.02      <0.001
                      0.08(0.05-0.12) 0.21(0.15-0.39)
   C14:0-DC           0.06 ± 0.01     0.11 ± 0.01      <0.001
                      0.07(0.03-0.1)  0.11(0.08-0.16)
   C14:1-2            0.1 ± 0.02      0.05 ± 0.01      0.037
                      0.09(0.03-0.27) 0.04(0.03-0.08)
   C15:2-DC           0.05 ± 0.01     0.08 ± 0.01      0.015
                      0.05(0.03-0.08) 0.08(0.04-0.13)
   C16                3.23 ± 0.44     6.71 ± 1.41      0.039
                      2.99(1.5-5.74)  4.78(2.83-14.91)
   C16:0-DC           0.22 ± 0.02     0.38 ± 0.04      0.003
                      0.21(0.13-0.31) 0.34(0.24-0.69)
   C16:1              0.92 ± 0.1      1.68 ± 0.23      0.009
                      0.83(0.52-1.32) 1.47(0.79-2.92)
   C16:1-DC           0.05 ± 0.01     0.1 ± 0.01       <0.001
                      0.05(0.03-0.08) 0.09(0.07-0.12)
   C17:1              0.04 ± 0        0.08 ± 0.01      0.015
                      0.04(0.02-0.06) 0.06(0.04-0.14)
   C18                1.92 ± 0.26     4.16 ± 0.68      0.01
                      1.87(0.86-3.78) 3.52(2.18-8.04)
   C18:1              5.54 ± 0.66     11.16 ± 2.08     0.026
                      5.53(2.79-8.9)  7.64(5.4-22.47)
   C18:1-OH           1.35 ± 0.18     2.01 ± 0.22      0.032
                      1.18(0.63-2.23) 1.86(1.11-3.29)
   C20:0              0.09 ± 0.01     0.19 ± 0.02      0.004
                      0.08(0.04-0.19) 0.19(0.05-0.29)
   C20:0-OH           0.06 ± 0.01     0.12 ± 0.01      0.001
                      0.06(0.04-0.09) 0.14(0.06-0.16)
   C20:2-OH-2         0.03 ± 0        0.05 ± 0.01      0.035
                      0.03(0.02-0.05) 0.06(0.02-0.08)
   C20:3              0.45 ± 0.05     0.62 ± 0.06      0.044
                      0.38(0.28-0.67) 0.53(0.36-1)
   [122]Open in a new tab

   SEM, Standard Error of Mean; min-max, minimum-maximum.

3.6. Correlation analysis on metabolomics and transcriptomics

   We further verified the relationship between metabolomics and
   transcriptomics under PM[2.5] exposure. Significant lipid
   metabolism-related differential metabolites were detected through
   pathway enrichment analysis with differential metabolites. Besides
   Leukotriene B4, lipid metabolism-related metabolites were significantly
   reduced ([123] Figure 4A ). Moreover, two signaling pathways
   (mTOR/AMPK) and two disease-associated gene pathways (type 2 diabetes,
   NAFLD) were identified through the enrichment of DEGs by KEGG. We
   analyzed and observed the correlation of the enriched DEGs in these
   pathways and added one other top enriched signaling pathway (PI3K-Akt),
   which showed that the IRS1 gene played a central role and the pathways
   were interrelated and influenced each other ([124] Figure 4B ). By
   performing a correlation analysis between the global DEM and DEG, using
   the standard of rho > 0.5 and p-value < 0.05, the results showed a
   broad correlation between DEM and DEG (Data not shown). To further
   observe the changes and correlations related to lipid metabolism, the
   screened metabolites related to lipid metabolism were used as the main
   nodes. The divergent selection of related DEGs was performed using the
   analysis criteria of rho > 0.80 and p-value < 0.005. A significant
   clustering was observed, with two centers formed around Leukotriene B4
   and other lipid metabolites ([125] Figure 4C ). We found that amino
   acid and carbohydrate metabolism in the KEGG pathway correlated ([126]
   Supplementary Figure 3 ).

Figure 4.

   [127]Figure 4
   [128]Open in a new tab

   The effect of PM[2.5] exposure on the correlation of DEMs and DEGs. (A)
   Relative expression of lipid metabolism-related DEMs. (B)
   Network(Pathway-Gene) of significant enrichment of DEGs by KEGG. (C)
   Association analysis of transcriptomic and metabolomic variation.
   Connection network between lipid metabolism-related DEMs and DEGs (rho
   > 0.80 and p-value < 0.005).

3.7. Effects of PM[2.5] exposure on disorders of lipid metabolism

   To further verify liver steatosis under PM[2.5] exposure, we performed
   Oil Red O staining of mice liver tissue to measure concentrations of
   intracellular triglyceride stored in the lipid droplets of hepatic
   cells, which was contrasted with quantitative analysis of the ratio of
   oil red positive regions to total cell area ([129] Figure 5A ). The
   PM[2.5]-treated group had higher lipid accumulation in the hepatocytes
   than the control group (***p<0.001). In addition, increases in hepatic
   triglyceride (TGs) and FFA levels were also observed in PM[2.5]-exposed
   group ([130] Figures 5B, C ). These results suggest that ambient
   PM[2.5]exposure results in the accumulation of lipids and elevation of
   FFA in the liver.

Figure 5.

   [131]Figure 5
   [132]Open in a new tab

   The effect of PM[2.5] exposure on hepatic lipid accumulation in mice.
   (A) Liver pathology was assessed by Oil Red O staining. (a) Control;
   (b) PM[2.5]; (c) Quantification of the percentage of liver oil
   deposits. (B) Quantitative enzyme-based assays of hepatic triglycerides
   (TG) levels. (C) Quantitative enzyme-based assays of hepatic free fatty
   acids (FFA) levels. Data represent more than three independent
   experiments (Mean ± SEM). Scale bar, 100 µm, and 50 µm. n.s not
   significant. * p < 0.05, *** p < 0.001.

   Furthermore, studies have shown that the AMPK signaling pathway, which
   is significantly enriched in the transcriptomics analysis of our data,
   may be one of the key pathways to regulating lipid metabolism and
   deposition. Related genes participate in adipogenesis, such as the
   activated sterol regulator-binding protein 1 (SREBP1) and the
   peroxisomal proliferative agent-activated receptor gamma (PPARγ). For
   further verification of lipid metabolic disorders, an
   immunofluorescence technique was performed to analyze the expression of
   SREBP1 and PPARγ.

   One of the key elements in hepatic metabolic disorder studies is
   SREBP1, a major lipogenic transcription factor. Over-activation and
   over-expression of SREBP1 can lead to an imbalance of lipid
   homeostasis, prone to triglyceride accumulation and cirrhosis
   ([133]46). Consistent with previous research ([134]47), significantly
   higher expressions of SREBP1 were revealed in the PM[2.5] exposed group
   compared with the control group (p<0.001) ([135] Figure 6A ).

Figure 6.

   [136]Figure 6
   [137]Open in a new tab

   The effect of PM[2.5] exposure on the expression of SREBP1, PPARγ, and
   PPARα in mice. (A) The immunofluorescence staining of SREBP1 and PPARγ.
   Nuclei were stained with DAPI. (a, d) Control; (b, e) PM[2.5]; (c, f)
   Graph quantifying SREBP1 and PPARγ. (B) The expression of PPARγ and
   SREBP1 after PM[2.5] treatment. (a) Western blotting of PPARγ and
   SREBP1. (b, c) The quantification of the expression levels of PPARγ and
   SREBP1. (C) Quantitative real-time PCR analysis of PPARα mRNA
   expression. (D) The expression of PPARα protein in liver tissue. (a)
   Western blotting of PPARα. (b) The quantification of PPARα protein
   level. β-Actin as a reference gene. Data represent more than three
   independent experiments (Mean ± SEM). Scale bar, 100 µm. n.s not
   significant. ** p < 0.01, *** p < 0.001. n = 3 mice per group.

   PPARγ is another key gene regulating fatty acid storage by activating
   genes that stimulate lipid ingestion and adipogenesis ([138]48). Our
   study showed that mice in the PM[2.5] exposure group decreased PPARγ
   expression significantly compared to the control group as detected by
   immunofluorescence assay ([139] Figure 6A ). To further verify the
   immunofluorescence results, western blot analysis was performed to
   detect the protein levels of SREBP-1 and PPAR-γ in hepatic tissues. The
   results showed that SREBP-1 was significantly upregulated, while the
   opposite results were observed in the PPARγ level ([140] Figure 6B ).

   Considering that PPARα plays an important role in the regulation of
   liver lipid metabolism ([141]49), the effect of PM[2.5] exposure on
   PPARα expression was evaluated by qPCR and western blotting. As shown
   in [142]Figure 6C , the level of PPARα mRNA in the liver of PM[2.5] was
   significantly reduced (p < 0.01). Then, Western blotting was performed
   to confirm the downregulation of PPARα ([143] Figure 6D ). These
   results indicated that PM[2.5]-induced liver lipid metabolic disorders
   might be related to the downregulation of PPARα and PPARγ expression
   via deceleration of lipoprotein transport and increased lipotoxicity
   ([144]50).

4. Discussion

   Air pollution PM[2.5] not only has a huge impact on the respiratory and
   cardiovascular systems but also seriously impacts the whole body and
   various organs through the plasma and into peripheral tissues
   ([145]51). Many researches have confirmed the central function of the
   active oxygen (ROS) and inflammatory factors in the regulation of
   PM[2.5] multi-organ toxicity. Because PM[2.5] is abundant in active
   oxidants like metals, PAHs, and quinones ([146] Supplementary Table 1
   ), it can induce ROS to form through lung redox reactions by direct
   interaction between PM[2.5] and pulmonary lining. Numerous experiments
   have also shown that PM[2.5] is responsible for forming lipid
   peroxidation products, and inflammatory factors that diffuse from the
   lungs could be transferred through the plasma and into peripheral
   tissues, where they could cause oxidative stress, inflammatory
   response, and, thus, damage ([147]3, [148]5, [149]26, [150]44,
   [151]52).

   As the liver is the core organ for detoxifying exogenous chemicals,
   liver damage caused by PM[2.5] exposure has attracted more and more
   attention in recent years ([152]17, [153]25, [154]28, [155]29). PM[2.5]
   could directly affect the process of normal hepatic function by
   enhancing inflammatory cytokines, ultimately elevating the risk of
   NAFLD by abnormal lipids metabolism ([156]44, [157]45). Besides,
   PM[2.5]-caused destruction of tight junctions allows intestinal
   bacteria and their toxic derivatives to leak, leading to liver
   inflammation and even the development of NASH ([158]53). A study by
   Wang ([159]54) has illustrated that long-term PM[2.5] exposure can
   contribute to enteral malnutrition and, subsequently, abnormal glucose
   metabolism, which leads to the transfer of lipopolysaccharides into the
   systemic circulation, exacerbating the process of NAFLD and T2DM
   ([160]44). More detailed functions need further verification and
   exploration to provide new insight into our knowledge of the potential
   molecular mechanism of hepatic injury by PM[2.5] exposure. Our study
   provides a comprehensive description and a partially targeted analysis
   of the differences in liver metabolism and gene expression under
   PM[2.5]exposure, especially lipid metabolic disorders.

   Previous studies have shown that dysregulation of hepatic lipid
   metabolism plays an important role in various metabolic diseases
   ([161]24, [162]55). Nonalcoholic fatty liver disease, the most common
   liver pathological change, is characterized by lipid accumulation and
   is closely associated with metabolic syndrome ([163]32, [164]33). Our
   study found that the pathways of NAFLD and T2DM were significantly
   enriched under PM[2.5] exposure by the analyses of transcriptome data.
   In previous studies, PM[2.5] exposure may accelerate the occurrence of
   lipid-related metabolic diseases ([165]27), such as T2DM and NAFLD
   ([166]21, [167]56), by inducing dyslipidemia ([168]52, [169]57) and
   adipose dysfunction ([170]4), while the results of our analysis provide
   correlative evidence of gene expression for it. The integrated
   metabolomics and transcriptomics results showed that amino acid,
   carbohydrate, and lipid metabolism were disordered ([171] Supplementary
   Figure 3 ).

   In this study, we further measured acylcarnitine levels in mice liver
   tissue, which is widely used to screen for metabolic diseases and
   identify some relevant biomarkers ([172]19, [173]58). Acylcarnitine
   plays an important role in maintaining normal liver function. As a
   specific substrate for mitochondrial fatty acid β-oxidation, it can
   help liver cells transfer fatty acids into mitochondria to provide
   energy for the body ([174]19, [175]59), which is one of the major
   pathways of lipid metabolism ([176]60, [177]61). When mitochondrial
   function is damaged, acylcarnitine accumulates, which can be a
   practical indicator for predicting hepatotoxicity ([178]62). Our
   results found a significant accumulation of short-chain, medium-chain,
   and long-chain acylcarnitine, consistent with a previous study finding
   that PM[2.5] exposure resulted in a significant accumulation of
   medium-chain acylcarnitine ([179]17). Acylcarnitine, with obvious
   differences and stability under PM[2.5] exposure among medium-chain or
   long-chain acylcarnitine, can be further used as a biomarker to
   indicate liver damage. Only myristoyl carnitine (C14:1) was decreased
   in statistical significance, but there was no relevant research to show
   its special mechanism in hepatocytes. At the same time, acylcarnitine
   accumulation also represents mitochondrial disorders to some extent and
   plays a key role in nonalcoholic steatohepatitis ([180]34).

   PPARs, which act as nuclear hormone receptors, regulate the
   transcription of genes associated with lipid metabolism and glucose
   metabolism.It has three isoforms: PPARα, PPARβ/δ and PPARγ. Numerous
   researches have demonstrated that PPARα negatively affects the
   proinflammatory and Acute Phase Reaction (APR) signaling pathways, as
   observed in the Systemic Inflammatory, Atherogenic, and Nonalcoholic
   Fatty Liver Disease (NASH) models ([181]63–[182]67). As a key
   transcriptional regulator of adipogenesis, PPARγ plays a key role in
   lipid storage and lipid droplet formation ([183]63, [184]68). For
   instance, previous studies have shown that exposure to PM[2.5] inhibits
   PPARγ signal transduction ([185]17), to which subsequent liver damage,
   such as triglyceride accumulation and hepatic steatosis ([186]46), can
   be partly attributed ([187]37). Zheng et al.’s studies showed that
   PM[2.5] induced abnormal lipid balance and decreased PPARγ and PPARα
   expression in vivo ([188]63) and in vitro ([189]37). In the present
   study, we also found that PPARα and PPARγ were significantly inhibited
   under PM[2.5] exposure, indicating an imbalance of lipid homeostasis
   induced by PM[2.5]. However, the related effect and mechanism of
   hepatic steatosis need to be further studied, which may become a key
   step to understanding the impact and prevention of PM[2.5] on liver
   injury.

   SREBP-1c is a major transcription factor that regulates hepatic de novo
   lipogenesis through insulin ([190]69). Furthermore, SREBP1 molecules
   mainly provide the building block by inducing lipid synthesis in
   rapidly growing cells ([191]46). Hepatic de novo lipogenesis can be
   reduced, and excessive lipid accumulation can be suppressed by
   modulating the AMPK/SREBP1c/FAS signaling pathway ([192]70). The role
   of SREBP-1c was demonstrated in a transgenic mouse model overexpressing
   SREBP-1c in the liver, which leads to the development of hepatic
   steatosis due to increased lipogenesis ([193]71). Our gene network
   ([194] Figure 4B ) shows that the core node IRS1 is also related to
   SREBP-1c. Previous studies have shown the decrease in IRS1 expression
   associated with increased fat synthesis and steatosis. Docking and
   phosphorylation of Irs1/Irs2 in the cell can activate downstream kinase
   cascades, such as the PI3K-Akt pathway. Akt, activated by the pathway,
   can further inhibit hepatic gluconeogenesis but simultaneously activate
   SREBP1c-mediated hepatic lipid metabolism ([195]72). In our study, it
   was demonstrated by fluorescent staining that PM[2.5] exposure leads to
   overexpression of SREBP1, promoting the development of steatosis, which
   is consistent with previous findings ([196]47).

   According to the KEGG database, we found that there were seven common
   significantly enriched pathways combining the metabolomics and
   transcriptomics analysis data between the PM[2.5] exposure group and
   control group, including alanine, aspartate and glutamate metabolism,
   phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine
   metabolism, retinol metabolism, tyrosine metabolism, cysteine, and
   methionine metabolism and glutathione metabolism. The thorough KEGG
   pathway of the relationship between differential metabolites and DEGs
   related to glutathione metabolism, retinol metabolism, alanine,
   aspartate and glutamate metabolism, and cysteine and methionine
   metabolism are shown in [197]Supplementary Figure 1 .

   Glutamate is essential to adjust glutathione levels in the body as the
   important substrate for synthesizing glutathione, which can undergo
   intermediate conversion by glutamine ([198]62). Our study significantly
   altered the expression of glutathione (GSH) and L-Glutamic acid at
   PM[2.5] exposure. Although oxidized glutathione (GSSG) was not a
   significantly altered metabolite, the GSH/GSSG ratio decreased with
   PM[2.5] exposure due to the significant decrease in GSH. The decreased
   ratio is the hallmark of oxidative stress in the hepatocytes ([199]17,
   [200]73), which is consistent with the previous study that exposure to
   PM[2.5] may cause oxidative stress in the liver ([201]26, [202]74).

   IRS1 is essential to regulate insulin-dependent glucose utilization and
   glycogen synthesis. The disruption of IRS1 signaling is a key and
   common mechanism in developing insulin resistance in population and
   laboratory studies ([203]75–[204]77). In our transcriptomic analysis,
   we found that the expression of the irs1 was significantly
   downregulated in the PM[2.5]-exposed group, suggesting that IRS1 might
   be an important pathway for preventing and intervening in
   PM[2.5]-accelerated metabolic liver diseases. This idea was supported
   by an earlier study, finding that total flavonoids alleviate
   PM[2.5]-induced NAFLD by modulating the IRS1/Akt and CYP2E1/JNK
   pathways ([205]78). Leukotriene B4 (LB4) could activate LB4r1 in
   hepatocytes, leading to cellular insulin resistance ([206]79). In a
   recent study, Li et al. suggest that the LB4/Lb4r1 axis promotes the
   development of NAFLD by enhancing lipogenesis in hepatocytes,
   potentially serving as a therapeutic target for NAFLD ([207]80). In the
   metabolomic analysis, we found LB4 was significantly upregulated in the
   PM[2.5]-treated group, indicating that LB4/Lb4r1 axis might participate
   in the PM[2.5]-induced abnormal liver metabolism. Thus, IRS1 and LB4
   might mediate the hepatic pathological impact of PM[2.5] exposure,
   which provides promising directions for developing therapeutic
   interventions.

   In addition, the other detrimental influence of PM[2.5] exposure can
   also be discovered with non-negligible abnormal phenomena shown in our
   study, including inflammatory response, insulin resistance, apoptosis,
   fibrosis, cofactor-related and vitamin-related metabolic disorders
   ([208]5, [209]37, [210]52, [211]66, [212]81). Since our experimental
   study used single-sex mice and the gender differences were not
   considered, which may be certain restrictions. In future studies, we
   will continue to strengthen research on female mice to analyze further
   the distinct effects of PM[2.5] exposure in both genders. As the
   metabolism and gene expression change depicted in our study, the
   specific effects and mechanisms of those changes need further
   exploration and experimental verification. The results may provide a
   comprehensive foundation and new insight into our knowledge of lipid
   accumulation and chronic hepatic injury by PM[2.5] exposure.

5. Conclusion

   Our study demonstrated that PM[2.5] could induce extensive metabolic
   disturbances, particularly significant in lipid and amino acid
   dysregulation, through in vivo experiments combined with metabolomics
   and transcriptomics analyses. Meanwhile, our results revealed lipid
   dysfunction and hepatic steatosis induced by PM[2.5] exposure,
   manifested as acylcarnitine and lipid droplet accumulation.
   Furthermore, we speculated and detected several key transcription
   factors as the potential regulatory effects in lipid metabolic
   disorders, PPARα, PPARγ, and SREBP1, and found their aberrant
   expression in the PM[2.5] exposure group. Our study provides a novel
   molecular and genetic basis for a better understanding of the
   mechanisms of hepatic metabolic disorders induced by PM[2.5] exposure,
   which can provide new insights into the toxicology of liver lipid
   metabolic disorders associated with air pollution and the risk
   assessment of chronic liver diseases.

Data availability statement

   The original contributions presented in the study are publicly
   available. This data can be found here:
   [213]https://github.com/Chenxiao-Zhang/RawData.

Ethics statement

   The animal study was approved by the animal and ethics review committee
   of the laboratory animal center at Shanghai Jiao Tong University School
   of Medicine (Shanghai, China). The study was conducted in accordance
   with the local legislation and institutional requirements.

Author contributions

   XD and CZ designed the study. CZ and TM drafted the manuscript. TM, JW,
   and DM conducted the PM2.5 collection and animal experiment. TM and CZ
   conducted liver histological staining. ML and XW completed the
   Quantitative Detection of Acylcarnitine. CZ, JW, and CL processed
   high-resolution mass spectrometric analysis and high-throughput RNA
   sequencing. CZ, JR, and XW contributed to virtualization. CZ, CL, DM,
   JW, and ML contributed to data analysis and manuscript writing. All
   authors contributed to the article and approved the submitted version.

Funding Statement

   This study was supported by the National Natural Science Foundation of
   China (Grant No. 21777099, XD), SJTU Public Health School Local
   High-level University Achievement-oriented Top-notch Cultivation
   Programme for Undergraduate Students (Grant No. 18ZYGW12), and Core
   Facility of Basic Medical Sciences, Shanghai Jiao Tong University
   School of Medicine. The funding agencies had no role in the design and
   conduct of the study; collection, management, analysis, and
   interpretation of the data; preparation, review, or approval of the
   manuscript; or decision to submit the manuscript for publication.

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [214]https://www.frontiersin.org/articles/10.3389/fendo.2023.1212291/fu
   ll#supplementary-material
   [215]Click here for additional data file.^ (1.4MB, doc)

References