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Ambient Ultrafine Particle Ingestion Alters Gut Microbiota in Association with Increased Atherogenic Lipid Metabolites


Ambient particulate matter (PM) exposure is associated with atherosclerosis and inflammatory bowel disease. Ultrafine particles (UFP, dp < 0.1–0.2 μm) are redox active components of PM. We hypothesized that orally ingested UFP promoted atherogenic lipid metabolites in both the intestine and plasma via altered gut microbiota composition. Low density lipoprotein receptor-null (Ldlr−/−) mice on a high-fat diet were orally administered with vehicle control or UFP (40 μg/mouse/day) for 3 days a week. After 10 weeks, UFP ingested mice developed macrophage and neutrophil infiltration in the intestinal villi, accompanied by elevated cholesterol but reduced coprostanol levels in the cecum, as well as elevated atherogenic lysophosphatidylcholine (LPC 18:1) and lysophosphatidic acids (LPAs) in the intestine and plasma. At the phylum level, Principle Component Analysis revealed significant segregation of microbiota compositions which was validated by Beta diversity analysis. UFP-exposed mice developed increased abundance in Verrocomicrobia but decreased Actinobacteria, Cyanobacteria, and Firmicutes as well as a reduced diversity in microbiome. Spearman’s analysis negatively correlated Actinobacteria with cecal cholesterol, intestinal and plasma LPC18:1, and Firmicutes and Cyanobacteria with plasma LPC 18:1. Thus, ultrafine particles ingestion alters gut microbiota composition, accompanied by increased atherogenic lipid metabolites. These findings implicate the gut-vascular axis in a atherosclerosis model.


Ultrafine particles (UFP, dp < 0.1–0.2 μm) are redox active components of airborne particulate matter (PM) that are enriched in transition metals and cycling organic chemicals1,2. In addition to inducing oxidative stress in human aortic endothelial cells3, UFP exposure reduces anti-oxidant capacity of plasma high-density lipoprotein (HDL) and increases oxidative lipid metabolism to accelerate atherosclerosis in low-density lipoprotein (LDL) receptor-knockout (Ldlr−/−) mice4,5. Recent epidemiological studies have linked ambient PM exposure with an increased risk for inflammatory bowel disease (IBD)6, and carotid intima thickness has also linked IBD with an increased risk for the development of atherosclerosis7. A large fraction of inhaled PM is recognized to be eliminated to the GI tract8,9. However, it remains elusive whether oral UFP ingestion induces intestinal inflammation to initiate atherosclerosis.

While the respiratory system is considered the primary route of inhaled PM exposure, the intestine is exposed to inhaled PM via mucociliary transport from the lungs to the gastrointestinal (GI) tract10,11. In the modern Western diet, more than 1012 UFP are orally ingested daily per person12. These ingested dietary UFP, including titanium oxide (TiO2) as whitening additives and alumino-silicates in drinking water, are absorbed by the intestinal intraepithelial lymphocytes that release pro-inflammatory cytokines to stimulate T-cell proliferation11,13,14,15,16. In addition to reducing the anti-oxidant capacity of HDL, UFP exposure induced intestinal release of pro-inflammatory mediators and fatty acid metabolites in Ldlr-null mice4,5. Urban coarse particulate matter (PM10, d < 10 μm) ingested via contaminated food altered gut microbiota in IL-10-null mice, an IBD mouse model17. For this reason, we sought to study the role of UFP ingestion on gut microbiota in Ldlr-null mice to alter lipid metabolism and atherogenic lipid metabolites.

The gut of human and many other vertebrates is mostly dominated by two phyla of bacteria, Bacteroidetes and Firmicutes18. Minor populations of Actinobacteria, Fusobacteria, and Cyanobacteria species are also present, as part of a complex microbial community18. Dysbiosis, an imbalance in the gut microbiota, may modulate host metabolism, immunity, and inflammatory responses resulting in pathological conditions. Mounting evidence has supported the link between the intestinal microbiome and human diseases, including cardiovascular, gastrointestinal, metabolic, neurological diseases, cancer, and obesity19,20,21,22,23,24,25,26,27. Gut microbiome-induced changes in lipid metabolism are associated with intestinal inflammation20,23,28,29 and gut microbiota-dependent formation of dietary trimethylamine (TMA) is linked with atherosclerosis25,30.

In this context, building on our previous inhalation study in which UFP exposure promoted inflammatory responses and lipid metabolism in both the gastrointestinal and vascular systems4,5, we hereby tested the hypothesis that oral UFP ingestion altered gut microbiota to promote intestinal and serum pro-inflammatory mediators and atherogenic lipid metabolites in Ldlr-null mice. Our findings suggest gut-vascular transmissibility via UFP-mediated changes in microbiome in a Ldlr-null mouse model of atherosclerosis.


UFP ingestion segregated gut microbiota

We analyzed the gut microbial composition in the UFP-ingested Ldlr-null mice by isolating DNA from cecum contents, followed by MiSeq 16S ribosomal RNA gene sequencing to characterize the microbiome. The relative abundance of bacteria (Fig. 1A) was calculated at the phylum level. Principal Component Analysis (PCA) revealed segregation of microbiota between the control and UFP-ingested groups. The Eigen vectors and values calculated from phylum level abundance revealed that Candidatus Saccharibacteria (TM7), Cyanobacteria, Chordata, Verrucomicrobia, and Spirochaetes were significantly different in the UFP-ingested group (Fig. 1B,C). In the first Principal Component (PC1), the mean value (red pentagram) of the UFP-ingested group was significantly lower than that of the control group (blue diamond) (p < 0.001, n = 11–12) (Fig. 1D).

Figure 1: Principle Component Analysis (PCA) of Microbiota.
figure 1

DNAs were isolated from cecal contents for miseq sequencing. PCA was conducted with microbiota data from vehicle control (n = 11) and UFP-gavaged mice (n = 12). (A) The abundance (natural logarithm value) of each bacterial phylum (27 phyla) for the individual mice. (B) The characteristic pattern exhibited from Eigen vectors was calculated from the abundance data. (C) Eigen values calculated from abundance data indicated the main variance was from Verrucomicrobia, Spirochaetes, Cyanobacteria, Chordata and Candidatus Saccharibacteria as mapped in the inset. (D) In the principal component (first two Eigen vectors) space, the control and UFP groups exhibited distinct characteristics. The mean UFP value (red pentagram) is significantly lower than that of the control (blue diamond) in the first principal component space (PC1, p < 0.001).

UFP ingestion altered the relative abundance of 4 out of the 27 phyla (Fig. 2A). The relative abundance of Verrucomicrobia (associated with intestinal mucus degradation) was increased by 133.4 ± 52.9% (n = 11–12, p < 0.05) (Fig. 2B), Firmicutes was decreased by 19.0 ± 3.5% (n = 11–12, p < 0.01) (Fig. 2C), Cyanobacteria was reduced by 97.7 ± 0.4% (n = 11–12, p < 0.05) (Fig. 2D) and Actinobacteria was lowered by 40.1 ± 6.9% (n = 11–12, p < 0.05) (Fig. 2E). Actinobacteria, Cyanobacteria, and Firmicutes are recognized to associate with fatty acid absorption and lipid metabolism in mice18.

Figure 2: Gavaged UFP Altered cecal microbiota.
figure 2

DNAs were isolated from cecal contents for miseq sequencing. The relative abundance of bacteria was calculated based on operational taxonomic units (OTUs). (A) Overview of the relative abundance of gut bacteria depicted at the phylum level in mice exposed to vehicle control vs. UFP. (B–E) Relative abundance of Verrocomicrobia, Firmicutes, Cyanobacteria, and Actinobacteria was plotted against the control (n = 11–12).

At the species level, PCA revealed significant segregation between the UFP-ingested and the control groups (PC1, p < 0.0001) (Supplemental Figure S1A). UFP ingestion significantly altered the relative abundance of 54 out of 675 operational taxonomic units (OTUs), which roughly correspond to species (Supplemental Figure S1B). The relative abundance of Akkermansia muciniphila, the dominant species of Verrucomicrobia (Supplemental Figure S1C), was increased by ~2.4-fold, and two Lachnospiraceae species, including Lachnoclostridium Clostridium saccharolyticum and Clostridium scindens, were decreased by ~10-fold and ~2.2-fold, respectively (Supplemental Figure S1D,E). The Lachnospiraceae family as a whole (negatively associated with colon cancer) was reduced by ~1.8-fold (Supplemental Figure S1F). PCA also revealed that the UFP-ingested group had statistically significant different microbiome at the Class, Order, Family and Genus levels (p < 0.0001).

To verify the UFP-mediated distinct microbial compositions, we re-analyzed the data using an independent bioinformatics pipeline. Alpha diversity analysis revealed that UFP treated mice had a less diverse microbiome as assessed by Chao1, Faith’s phylogenetic diversity, and Shannon index (Supplemental Figure S2A). Beta diversity analysis using unweighted and weighted UniFrac further demonstrated statistically significant separation of microbial profiles between control and UFP-treated mice as assessed by a permutation-based method (Supplemental Figure S2B). Differential abundance testing using negative binomial models for OTU level data revealed that 29 OTUs were enriched and 53 were depleted in the UFP treated mice compared to control at a false discovery rate (FDR) threshold of 0.1 (Supplemental Figure S2C). This included five enriched OTUs identified as Akkermansia muciniphila, three depleted Cyanobacteria OTUs in the order YS2 and one depleted Actinobacteria OTU in the Bifidobacterium genus. Analysis at the phylum level confirmed enrichment of Verrucomicrobia and depletion of Cyanobacteria and Actinobacteria at a FDR threshold of 0.1.

UFP ingestion increased intestinal inflammatory cells and plasma cytokines

Orally gavaged UFP increased F4/80 staining for macrophages by 3-fold (Control = 0.07 ± 0.01 vs. UFP = 0.2 ± 0.04, n = 8–11, p < 0.05) and anti-Ly6G staining for neutrophils by 2-fold (Control = 0.027 ± 0.0095 vs. UFP = 0.056 ± 0.0128, n = 8–11, p = 0.1) in the intestinal villi, though only the change in macrophages reached significance (Fig. 3). In parallel, both TNF-α and MCP-1 levels were elevated by 2-fold in the plasma, though the change in MCP-1 fell short of significance (TNF-α: Control = 40.3 ± 7.3, UFP = 95.2 ± 23.1 pg/mL, n = 7, p < 0.05; MCP-1: Control = 334.2 ± 33.3, UFP = 653.7 ± 160.9 pg/mL, n = 7, p = 0.08) (Fig. 4A,B).

Figure 3: UFP ingestion promoted intestinal inflammation.
figure 3

Cross sections of ileum from mice exposed to vehicle control or UFP were stained with antibody F4/80 for macrophages and antibody against Ly6G for neutrophil. (A) Representative macrophage staining in the villi of ileum. (B) The averaged staining intensity of macrophages. (C) Representative neutrophil staining in the villi of ileum. (D) The averaged staining intensity of neutrophil. UFP ingestion significantly increased macrophage staining and exhibited a trend toward an increase in neutrophil stainnig (n = 7–11).

Figure 4: Gavaged UFP increased pro-inflammatory cytokines and lipid metabolites.
figure 4

Plasma levels of cytokine were measured by Luminex assay and the levels of lipid metabolites by LC-ESI-MS-MS. (A) Plasma TNF-α (n = 7). (B) Plasma MCP-1 (n = 7). (C) Intestinal LPC18:1 (n = 11–12). (D) Intestinal LPC18:0 (n = 11–12). (E) Plasma LPC18:1 (n = 11). (F) Plasma LPC18:0 (n = 11). Gavaged UFP significantly increased plasma TNF-α and LPC18:1 levels in both intestinal and plasma, and a trend of increase in MCP-1 (p = 0.0756, n = 7), whereas LPC 18:0 level was unchanged (n = 11–12).

Mass spectrometry analysis revealed elevated pro-atherogenic lipid metabolites, including lysophosphatidylcholine (LPC), lysophosphatidic acid (LPA), 1-palmitoyl-2-epoxyisoprostane E2-sn-glycero-3-phosphorylcholine (PEIPC), 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphorylcholine (POVPC) and 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphorylcholine (PGPC)31,32,33,34. LPC 18:1 was elevated by 1.8 fold in the intestine and ~6.5-fold in the plasma (intestine: Control = 32.8 ± 3.8, UFP = 60.2 ± 7.7 μg/g tissue, n = 11–12, p < 0.01; plasma: Control = 34.7 ± 2.1, UFP = 227.0 ± 11.7 μg/mL, n = 11, p < 0.001) (Fig. 4C,E), whereas LPC 18:0 remained unchanged (Fig. 4D,F). Furthermore, LPAs, including LPA18:1, LPA18:2 and LPA20:4, and oxidized phospholipids (PEIPC, POVPC, and PGPC) were significantly increased in the intestine and plasma (Supplemental Material Table S2). Hence, UFP ingestion promoted inflammatory responses in the intestine and increased various lipid metabolites, including the atherogenic LPC 18:1, in both the intestine and plasma.

UFP ingestion decreased cholesterol metabolism in cecum

Cecal bile acids, cholesterol and its metabolite, coprostanol, were quantified. While there were no significant changes in the plasma cholesterol (Supplemental Figure S3) or cecal bile acids (Supplemental Material Table S3), cecal cholesterol was increased by 1.25-fold (Control = 3783.3 ± 289.9, UFP = 4715.3 ± 234.7 μg/gram cecal contents, n = 11–12, p < 0.05), whereas the cholesterol metabolite coprostanol was significantly reduced by 69% (Control = 912.2 ± 217.3, UFP = 283.7 ± 109.5 μg/g cecal contents, n = 11–12, p < 0.05) (Fig. 5).

Figure 5: UFP ingestion modulated cecal cholesterol and its metabolites.
figure 5

Lipids from cecal contents were extracted by ethanol as described in methods. Cholesterol and cholesterol metabolite coprostanol were measured by gas chromatography. (A) Cecal cholesterol was elevated and (B) Cecal coprostanol was reduced by UFP ingestion (n = 11–12).

Association between UFP-altered microbiota and lipid metabolites

Spearman’s correlation analyses revealed that Actinobacteria was inversely correlated with intestinal cholesterol (ρ = −0.502, p < 0.05) (Fig. 6A), but positively correlated with coprostanol (ρ = 0.464, p < 0.05) (Fig. 6B). Actinobacteria (ρ = −0.559, p < 0.01), Cyanobacteria (ρ = −0.486, p < 0.05), and Firmicutes (ρ = −0.560, p < 0.01) were inversely correlated with plasma LPC 18:1 (Fig. 6C–F). Taken together, UFP-mediated reductions in Actinobacteria, Cyanobacteria, and Firmicutes are associated with pro-inflammatory cytokines and atherogenic lipid metabolites.

Figure 6: Correlation between microbiota and pro-inflammatory mediators.
figure 6

(A) The abundance of Actinobacteria was inversely correlated with cecal cholesterol. (B) Actinobacteria was positively correlated with coprostanol. (C) Actinobacteria was inversely correlated with intestinal LPC18:1. (D) Actinobacteria was inversely correlated with plasma LPC18:1. (E) Plasma LPC18:1 was negatively associated with Cyanobacteria. (F) Plasma LPC18:1 was negatively associated with Firmicutes (n = 11–12).


We demonstrate that chronic oral UFP ingestion to Ldlr-null mice engendered dysbiosis, including altered microbial composition and diversity in association with increased TNF-α and atherogenic LPC 18:1 and LPAs in the intestinal and plasma. Chronic UFP ingestion reduced the abundance of Lachnospiraceae (negatively associated with colon cancer)35,36,37 and increased Verrucomicrobia (intestinal mucus degradation)38,39 in the cecum. The reduction in Actinobacteria, Cyanobacteria, and Firmicutes was correlated with increased cecal cholesterol, and both intestinal and plasma levels of the lipid metabolite LPC 18:1. Thus, our finding provides new insight into gut-vascular transmissibility to initiate atherosclerosis via UFP-mediated segregation in microbiota.

Gut microbiota composition varies in natural populations and is influenced by environmental factors that regulate host metabolism, immunity, and inflammatory responses20,23,24,25,26,27. In our atherosclerosis model (UFP-ingested Ldlr-null mice), we observed an increased abundance in Verrucomicrobia (implicated in intestinal mucus degradation39) and a decreased abundance in Firmicutes (Fig. 2C), with a trend toward increased Bacteroidetes (data not shown). These findings are concordant with those of Kish et al., who reported an increase in the abundance of Verrucomicrobia and Firmicutes, but a decrease in Bacteroidetes, in PM10-exposed IL-10-null mice (an IBD model)17. In contrast, UFP-ingested atherosclerotic mice further developed a decreased abundance in Actinobacteria and Cyanobacteria, (Fig. 2D,E), whereas PM10-exposed IBD mice had no changes17. These differences in microbiota composition highlight the variations in PM sources, size and chemical compositions, as well as exposure duration, diet content, and animal models.

Gut microbiota influences numerous aspects of host energy and metabolism, including lipid metabolism40,41. A large portion of body cholesterol is synthesized in the intestine42, and conversion of cholesterol to bile acids is critical for maintaining cholesterol homeostasis and preventing accumulation of cholesterol, triglycerides, and toxic metabolites in the liver and other organs43. We previously showed that whole-body inhalation of UFP in Ldlr-null mice increased both intestinal and plasma lipid metabolites, including phospholipid lysophosphatidic acid (LPA), to promote atherosclerosis4,5. In the present study, UFP ingestion elevated pro-inflammatory lipid metabolites, including LPC, LPA, PEIPC, POVPC and PGPC (Fig. 4 and Supplemental Table S2). UFP ingestion also increased both intestinal and plasma LPC18:1 (Fig. 4D–G). Unsaturated LPC such as LPC18:1 is converted to inflammatory and atherogenic LPA by autotoxin to promote atherosclerosis33. Chattopadhyay et al. recently reported that Ldlr-null mice fed with LPC18:1 developed increased intestinal and systemic inflammation44, implicating LPC18:1 in UFP-mediated atherogenic responses.

At the family level, UFP ingestion decreased the relative abundance of Lachnospiraceae and a few species in the family (Supplemental Figure S1D–F) that are positively associated with diabetes, IBD, cirrhosis and prostate cancer45,46,47,48,49, but are negatively associated with colorectal cancer35,36,37. Intestinal inflammation promotes tumorigenesis by altering microbial composition and by inducing the expansion of microorganisms with genotoxic capabilities50.

Interestingly, Spearman’s analyses revealed that Actinobacteria was negatively correlated with intestinal and plasma LPC18:1 levels and cecal cholesterol levels (Fig. 6). These findings were in agreement with a recent report that decreased abundance in Actinobacteria was associated with increased cecal cholesterol excretion40. Furthermore, decreased abundance of cecal Cyanobacteria and Firmicutes was correlated with increased plasma LPC18:1 (Fig. 6E,F)51. This correlation is consistent with the relationship between obesity and the ecology of microbiota enriched in Actinobacteria, Cyanobacteria, and Firmicutes18,52,53,54. Taken together, our findings support the notion that UFP ingestion reduced Actinobacteria, Cyanobacteria, and Firmicutes to increase atherogenic lipid metabolites.

Alterations in gut permeability disrupt intestinal immune homeostasis55. PM ingestion increased gut permeability as assessed using FD4 or lactulose/mannitol gavage17,56. Analogous to UFP inhalation4,5, a moderate dose of UFP (PM0.1) ingestion at 40 μg/mouse/day, 3 days a week for 10 weeks, induced both intestinal and vascular pro-inflammatory mediators in the Ldlr-null mice (Figs 3 and 4). In addition, short-term PM (1 to 14 days) exposure was reported to increase intestinal inflammatory markers in association with increased gut permeability17,52 whereas our long-term exposure (10 weeks, 3 times/week) did not significantly alter gut permeability (in vivo and ex vivo) (Supplemental Figure S4A,B,C). In vitro, we observed that the colonic epithelial cell line, Caco-2, developed a dose-dependent increase in permeability to Straptavidin-HRP (97 kDa) at both 25 and 50 μg/ml of UFP treatment that is consistent with the findings of short term in vivo exposure (Supplemental Figure S4D). UFP ingestion increased Akkermansia muciniphila, the dominant Verrucomicrobia (Supplemental Figure S1C) that adheres to enterocytes and strengthens the integrity of the intestinal epithelium39. This may counterbalance the short-term increased permeability induced by UFP. Thus, the duration of UFP ingestion may influence gut epithelial permeability in the setting of altered microbiota composition.

For future studies, we would consider varying the UFP dose to recapitulate the range of human exposure. Given that gut permeability appears to be dependent on the duration of PM exposure, we are also interested in comparing acute versus chronic exposure to elucidate the possible protective effect of Akkermansia39. Based on our previous findings using an atherosclerosis model4,5, we elected to use Ldlr-null mice on a high fat diet in this study to demonstrate UFP-mediated alteration of the gut microbiota. In follow-up studies we will consider comparing the effects of UFP exposure in the setting of a high-fat or normal chow diet to determine how PM interacts with diet to shape the microbiome. The use of germ-free and antibiotic-treated mice would further elucidate the transmissibility through the microbiome of the intestinal and vascular phenotypes of UFP exposure.

Gut-vascular transmissibility is an emerging mechanism underlying the risk factor-mediated cardiovascular diseases. UFP ingestion by Ldlr-null mice induced decreased abundance of Actinobacteria, Cyanobacteria, and Firmicutes that correlated with the increased level of LPC18:1. Thus, we provide new gut-vascular insights into how PM affects microbiota composition and atherogenic mediators.


Ethics statement

All animal experiments were performed in compliance with UCLA Institutional Animal Care and Use Committee (IACUC) protocols, under a project license also approved by the UCLA IACUC. Humane care and use of animals were observed to minimize distress and discomfort.

Collection and chemical analysis of UFP

Ultrafine particles (UFP, defined as particles with an aerodynamic diameter less than 0.8 μm) were collected on 8″ × 10″ Teflon filters using a high-volume ultrafine particle (HVUP) sampler57 at flow rate of 400 L/min, about 150 meters downwind of the I-110 Freeway in central Los Angeles, during January and February of 2015. The UFP represent a mixture of pollution sources, including fresh ambient PM from areas impacted by heavy-duty diesel trucks, light duty gasoline vehicles and ship emissions, as well as PM generated by photochemical oxidation of primary organic vapors58. The collection and characterization of chemical composition was previously described5. Mass fraction of the measured chemical species (in units of ng/μg UFP), including organic matter (estimated as total organic carbon content based on the method by Turpin et al.)59 and individual elements and metals, are reported in Supplemental Table S1. The most abundant elements in our samples included Ca (29.4 ng/μg PM), Na (23.2 ng/μg PM), S (22.6 ng/μg PM), Al (11.3 ng/μg PM) and Fe (10.0 ng/μg PM). As shown in Table S1, organic matter and metals/trace elements had cumulative mass fractions of 197 ng/μg PM and 116 ng/μg PM (i.e. about 20% and 12% of the UFP mass, respectively). The remaining UFP mass is mostly comprised of inorganic ions (most importantly nitrate, sulfate and ammonium)60,61. While these ions constitute a large fraction of the UFP mass, toxic properties of UFPs in the Los Angeles basin as well as most other urban areas of the world is primarily driven by the organic compounds and redox active metal species62 and therefore the chemical analyses in this study were focused on these UFP fractions.

Mouse exposure to gavaged UFP

Age and weight-matched male low density lipoprotein receptor-null mice (Ldlr–/–) (at the age of 90 days and an average weight of 24.76 ± 0.37 g) on a C57BL/6 background (stock #002207, Jackson Laboratory, FA) were grouped randomly, and were orally administered with either vehicle control (11 mice) or 40 μg ambient UFP (12 mice) in 100 μL saline 3 days per week for 10 weeks via 22 gauge gavage needles. The orally administrated dosage of UFP at about 1.6 ug/g per session was determined based on our previous inhalation exposure at 400 μg/m3 assuming humans inhale 16 m3 air per day63. This dosage is about 5–10 folder lower than reported studies by Mutlu et al. and Kish et al.17,56. The mice were fed a Western-type high-fat diet (TD88137, 21% milk fat, 0.2% cholesterol, Harlan Laboratory) throughout the exposure period.

Measurement of plasma cytokines

Mice were euthanized by inhalation of isofluorane and cervical dislocation following completion of the 10 weeks exposure. Plasma was collected using plasma separators (BD Biosciences) as previously described64. Plasma levels of TNF-α, MCP-1 and IL-6 were analyzed by a Luminex assay (Millipore: MCYTOMAG-70K-04. Mouse Cytokine MAGNETIC Kit).


Ileum segments were harvested, fixed in PBS/4% paraformaldehyde, and embedded in paraffin blocks. Macrophages and neutrophils were stained using F4/80 antibody (Invitrogen, diluted at 1:100) and antibody against Ly6G (Biolegend, diluted at 1:100), respectively65. Intensity of F4/80 and Ly6G staining was quantified by the NIH ImageJ software ( and presented as staining to background ratio.

Quantification of lipid metabolites

Approximately 0.2–0.6 ml of blood was drawn for plasma preparation using plasma separators (BD Biosciences). The small intestines were dissected and a portion of the ileum was cut out and rinsed with cold saline. The extraction of lipid contents and measurement of lipid metabolites in plasma and intestine extracts were performed as previously described4,64. Lysophsophotidylcholine (LPC), lysophosphatidic acid (LPA), and oxidized phospholipids including 1-palmitoyl-2-epoxyisoprostane E2-sn-glycero-3-phosphorylcholine (PEIPC), 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphorylcholine (POVPC), and 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphorylcholine (PGPC) were analyzed and quantified by liquid chromatography, electron spray ionization, and tandem mass spectrometry as described previously (LC-ESI-MS/MS)4,33,64.

Analysis of cecal bile acids and sterols

Cecum content was suspended in methanol, vortexed for 10 min, centrifuged at 14,000 g for 10 min at room temperature, and the supernatant was collected. Silylation of sterols and bile acids was carried out simultaneously using supernatant. After silylation, samples were centrifuged to collect supernatants for gas chromatography (GC). Samples were injected into RTX-5 column (Restek corp. 30 m × 0.25 mm × 0.25 i.d.) and simultaneous quantification of sterols and bile acids was performed using GC (Agilent 7890A) coupled to a FID. Peak identification was based on comparison of retention times with commercial standards of 5α-cholestane (internal standard), 5β-cholanic acid (internal standard), cholesterol, coprostanol, cholestanol, cholic acid, deoxycholic acid, chenodeoxycholic acid and lithocholic acid.

Analysis of gut microbes via MiSeq sequencing

Cecum contents were used for DNA extraction using a commercial extraction system (PowerSoil DNA isolation kit, MO Bio laboratories, Inc). The quality of the DNA samples was confirmed using a Bio-Rad Experion system (Bio-Rad Laboratories, CA, USA). The 16S rRNA gene V4 variable region PCR primers 530/926 with barcode on the forward primer were used in a 30 cycle PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, USA) under the following conditions: 94 °C for 3 min, followed by 28 cycles of 94 °C for 30 s, 53 °C for 40 s and 72 °C for 1 min, after which a final elongation step at 72 °C for 5 min was performed. Following amplification, PCR products were checked in 2% agarose gel to determine the success of amplification and the relative intensity of bands. Sequencing was performed at MR DNA (, Shallowater, TX, USA) on a MiSeq following the manufacturer’s guidelines. Sequence data were processed using a proprietary analysis pipeline (MR DNA, Shallowater, TX, USA). Operational taxonomic units (OTUs) were defined by clustering at 3% divergence (97% similarity). Final OTUs were taxonomically classified using BLASTn against a curated Green Genes database66.

Principle component analysis (PCA) of microbiota

The dimensionality of the interrelated variables exhibiting the bacteria abundance was reduced by PCA67. Due to huge abundance differences among the bacteria, natural logarithm value of the abundance was input to the PCA to form a matrix of the number of bacteria in the taxonomic classification and the number of test samples. After obtaining the Eigen values and the Eigen vectors, the test samples were re-plotted in the coordinates of constructed by the first two Eigen vectors. The control and UFP groups were separated in the first two principal components. The PCA was applied to different taxonomic classifications; namely, phylum, class, order, family, genus, and species, respectively.

Statistical analysis

Data were expressed as mean ± SEM unless otherwise stated. Statistical analysis was done with Matlab or Graphpad Prism. Multiple comparisons were performed by one-way analysis of variance (ANOVA), and statistical significance for comparison between two groups was determined by student t-test or Wilcoxon rank-sum test when data was not normally distributed. The association of lipids and metabolites with gut microbes was assessed by Spearman’s rank correlation analysis among all animals in both control and UFP exposed groups. A p-value < 0.05 was considered statistically significant. Sample size for different assays may differ from original mouse numbers due to issues of sample preparation, capacity of device, or sample limitations, which were specified per individual assay.

Additional Information

How to cite this article: Li, R. et al. Ambient Ultrafine Particle Ingestion Alters Gut Microbiota in Association with Increased Atherogenic Lipid Metabolites. Sci. Rep. 7, 42906; doi: 10.1038/srep42906 (2017).

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


  1. Nel, A., Xia, T., Madler, L. & Li, N. Toxic potential of materials at the nanolevel. Science 311, 622–627 (2006).

    CAS  ADS  Article  PubMed  Google Scholar 

  2. Zhang, Y., Schauer, J. J., Shafer, M. M., Hannigan, M. P. & Dutton, S. J. Source apportionment of in vitro reactive oxygen species bioassay activity from atmospheric particulate matter. Environmental Science & Technology 42, 7502–7509 (2008).

    CAS  ADS  Article  Google Scholar 

  3. Li, R. et al. Ultrafine particles from diesel engines induce vascular oxidative stress via JNK activation. Free Radic Biol Med 46, 775–782, doi: S0891-5849(08)00750-8 (2009).

    CAS  Article  PubMed  Google Scholar 

  4. Li, R. et al. Effect of exposure to atmospheric ultrafine particles on production of free Fatty acids and lipid metabolites in the mouse small intestine. Environ Health Perspect 123, 34–41, doi: 10.1289/ehp.1307036 (2015).

    CAS  Article  PubMed  Google Scholar 

  5. Li, R. et al. Ambient ultrafine particles alter lipid metabolism and HDL anti-oxidant capacity in LDLR-null mice. J Lipid Res 54, 1608–1615, doi: 10.1194/jlr.M035014 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Kaplan, G. Air pollution and the inflammatory bowel diseases. Inflamm Bowel Dis 17, 1146–1148, doi: 10.1002/ibd.21449 (2011).

    Article  PubMed  Google Scholar 

  7. Doherty, R. Risk of atherosclerosis in patients with inflammatory bowel disease. Nat Clin Pract Gastroenterol Hepatol 3, 6–7 (2006).

    Google Scholar 

  8. Danese, S. & Fiocchi, C. Atherosclerosis and inflammatory bowel disease: sharing a common pathogenic pathway? Circulation 107, e52 (2003).

    Article  PubMed  Google Scholar 

  9. Yarur, A. J. et al. Inflammatory bowel disease is associated with an increased incidence of cardiovascular events. Am J Gastroenterol 106, 741–747, doi: 10.1038/ajg.2011.63 (2011).

    ADS  Article  PubMed  Google Scholar 

  10. Moller, W. et al. Mucociliary and long-term particle clearance in the airways of healthy nonsmoker subjects. J Appl Physiol 97, 2200–2206, doi: 10.1152/japplphysiol.00970.2003 (2004).

    Article  PubMed  Google Scholar 

  11. Moller, W., Haussinger, K., Ziegler-Heitbrock, L. & Heyder, J. Mucociliary and long-term particle clearance in airways of patients with immotile cilia. Respir Res 7, 10, doi: 1465-9921-7-10 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Great Britain. Ministry of Agriculture, F. & Food. Dietary intake of food additives in the UK: initial surveillance (HMSO, 1993).

  13. Beamish, L. A., Osornio-Vargas, A. R. & Wine, E. Air pollution: An environmental factor contributing to intestinal disease. J Crohns Colitis 5, 279–286 (2011).

    Article  PubMed  Google Scholar 

  14. Lomer, M. C., Thompson, R. P. & Powell, J. J. Fine and ultrafine particles of the diet: influence on the mucosal immune response and association with Crohn’s disease. Proc Nutr Soc 61, 123–130 (2002).

    Article  PubMed  Google Scholar 

  15. Nemmar, A., Hoylaerts, M. F., Hoet, P. H. & Nemery, B. Possible mechanisms of the cardiovascular effects of inhaled particles: systemic translocation and prothrombotic effects. Toxicol Lett 149, 243–253, doi: 10.1016/j.toxlet.2003.12.061 (2004).

    CAS  Article  PubMed  Google Scholar 

  16. Takenaka, S. et al. Distribution pattern of inhaled ultrafine gold particles in the rat lung. Inhal Toxicol 18, 733–740 (2006).

    CAS  Article  PubMed  Google Scholar 

  17. Kish, L. et al. Environmental particulate matter induces murine intestinal inflammatory responses and alters the gut microbiome. PLoS One 8, e62220, doi: 10.1371/journal.pone.0062220 (2013).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  18. Backhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA 101, 15718–15723, doi: 0407076101 (2004).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  19. Bull, M. J. & Plummer, N. T. Part 1: The Human Gut Microbiome in Health and Disease. Integr Med (Encinitas) 13, 17–22 (2014).

    Google Scholar 

  20. Cavalcante-Silva, L. H., Galvao, J. G., da Silva, J. S., de Sales-Neto, J. M. & Rodrigues-Mascarenhas, S. Obesity-Driven Gut Microbiota Inflammatory Pathways to Metabolic Syndrome. Front Physiol 6, 341, doi: 10.3389/fphys.2015.00341 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci 13, 701–712, doi: 10.1038/nrn3346 (2012).

    CAS  Article  PubMed  Google Scholar 

  22. Jia, W., Li, H., Zhao, L. & Nicholson, J. K. Gut microbiota: a potential new territory for drug targeting. Nat Rev Drug Discov 7, 123–129, doi: 10.1038/nrd2505 (2008).

    CAS  Article  PubMed  Google Scholar 

  23. Org, E., Mehrabian, M. & Lusis, A. J. Unraveling the environmental and genetic interactions in atherosclerosis: Central role of the gut microbiota. Atherosclerosis 241, 387–399, doi: 10.1016/j.atherosclerosis.2015.05.035 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Shulzhenko, N. et al. Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut. Nat Med 17, 1585–1593, doi: 10.1038/nm.2505 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Tang, W. H. & Hazen, S. L. The contributory role of gut microbiota in cardiovascular disease. J Clin Invest 124, 4204–4211, doi: 10.1172/JCI72331 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Wu, G. D. The Gut Microbiome, Its Metabolome, and Their Relationship to Health and Disease. Nestle Nutr Inst Workshop Ser 84, 103–110, doi: 10.1159/000436993 (2016).

    Article  PubMed  Google Scholar 

  27. Yamashita, T. et al. Intestinal Immunity and Gut Microbiota as Therapeutic Targets for Preventing Atherosclerotic Cardiovascular Diseases. Circ J 79, 1882–1890, doi: 10.1253/circj.CJ-15-0526 (2015).

    CAS  Article  PubMed  Google Scholar 

  28. Caesar, R., Nygren, H., Oresic, M. & Backhed, F. Interaction between dietary lipids and gut microbiota regulates hepatic cholesterol metabolism. J Lipid Res 57, 474–481, doi: 10.1194/jlr.M065847 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Velagapudi, V. R. et al. The gut microbiota modulates host energy and lipid metabolism in mice. J Lipid Res 51, 1101–1112, doi: 10.1194/jlr.M002774 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Wang, D. et al. Gut microbiota metabolism of anthocyanin promotes reverse cholesterol transport in mice via repressing miRNA-10b. Circ Res 111, 967–981, doi: CIRCRESAHA.112.266502 (2012).

    CAS  ADS  Article  PubMed  Google Scholar 

  31. Berliner, J. A., Subbanagounder, G., Leitinger, N., Watson, A. D. & Vora, D. Evidence for a role of phospholipid oxidation products in atherogenesis. Trends Cardiovasc Med 11, 142–147 (2001).

    CAS  Article  PubMed  Google Scholar 

  32. Leitinger, N. Oxidized phospholipids as modulators of inflammation in atherosclerosis. Curr Opin Lipidol 14, 421–430, doi: 10.1097/01.mol.0000092616.86399.dc (2003).

    CAS  Article  PubMed  Google Scholar 

  33. Navab, M. et al. Source and role of intestinally derived lysophosphatidic acid in dyslipidemia and atherosclerosis. J Lipid Res 56, 871–887, doi: 10.1194/jlr.M056614 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Navab, M. et al. Transgenic 6F tomatoes act on the small intestine to prevent systemic inflammation and dyslipidemia caused by Western diet and intestinally derived lysophosphatidic acid. J Lipid Res 54, 3403–3418, doi: 10.1194/jlr.M042051 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Wang, T. et al. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J 6, 320–329, doi: 10.1038/ismej.2011.109 (2012).

    CAS  Article  PubMed  Google Scholar 

  36. Zackular, J. P. et al. The gut microbiome modulates colon tumorigenesis. MBio 4, e00692–00613, doi: 10.1128/mBio.00692-13 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Zackular, J. P., Rogers, M. A., Ruffin, M. T. t. & Schloss, P. D. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res (Phila) 7, 1112–1121, doi: 10.1158/1940-6207.CAPR-14-0129 1940–6207 (2014).

    CAS  Article  Google Scholar 

  38. Png, C. W. et al. Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol 105, 2420–2428, doi: 10.1038/ajg.2010.281 (2010).

    CAS  ADS  Article  PubMed  Google Scholar 

  39. Reunanen, J. et al. Akkermansia muciniphila Adheres to Enterocytes and Strengthens the Integrity of the Epithelial Cell Layer. Appl Environ Microbiol 81, 3655–3662, doi: 10.1128/AEM.04050-14 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Martinez, I. et al. Diet-induced alterations of host cholesterol metabolism are likely to affect the gut microbiota composition in hamsters. Appl Environ Microbiol 79, 516–524, doi: 10.1128/AEM.03046-12 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63, doi: 10.1038/nature09922 (2011).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  42. Brunham, L. R. et al. Intestinal ABCA1 directly contributes to HDL biogenesis in vivo . J Clin Invest 116, 1052–1062, doi: 10.1172/JCI27352 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Chiang, J. Y. Bile acid metabolism and signaling. Compr Physiol 3, 1191–1212, doi: 10.1002/cphy.c120023 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chattopadhyay, A. et al. Tg6F ameliorates the increase in oxidized phospholipids in the jejunum of mice fed unsaturated LysoPC or WD. J Lipid Res 57, 832–847, doi: 10.1194/jlr.M064352 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Bajaj, J. S. et al. Gut Microbiota Alterations can predict Hospitalizations in Cirrhosis Independent of Diabetes Mellitus. Sci Rep 5, 18559, doi: 10.1038/srep18559 (2015).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  46. Kameyama, K. & Itoh, K. Intestinal colonization by a Lachnospiraceae bacterium contributes to the development of diabetes in obese mice. Microbes Environ 29, 427–430, doi: 10.1264/jsme2.ME14054 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Li, N., Xia, T. & Nel, A. E. The role of oxidative stress in ambient particulate matter-induced lung diseases and its implications in the toxicity of engineered nanoparticles. Free Radical Biology and Medicine 44, 1689–1699 (2008).

    CAS  Article  PubMed  Google Scholar 

  48. Maukonen, J. et al. Altered Fecal Microbiota in Paediatric Inflammatory Bowel Disease. J Crohns Colitis 9, 1088–1095, doi: 10.1093/ecco-jcc/jjv147 (2015).

    Article  PubMed  Google Scholar 

  49. Yu, H. et al. Urinary microbiota in patients with prostate cancer and benign prostatic hyperplasia. Arch Med Sci 11, 385–394, doi: 10.5114/aoms.2015.50970 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Arthur, J. C. et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338, 120–123, doi: 10.1126/science.1224820 (2012).

    CAS  ADS  Article  PubMed  PubMed Central  Google Scholar 

  51. Ku, C. S. et al. Edible blue-green algae reduce the production of pro-inflammatory cytokines by inhibiting NF-kappaB pathway in macrophages and splenocytes. Biochim Biophys Acta 1830, 2981–2988, doi: 10.1016/j.bbagen.2013.01.018 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Harwood, J. L. & J., A. L. Lipid Metabolism in Algae. Advances in Botanical Research (1989).

  53. Liu, X., Sheng, J. & Curtiss, R. 3rd Fatty acid production in genetically modified cyanobacteria. Proc Natl Acad Sci USA 108, 6899–6904, doi: 10.1073/pnas.1103014108 (2011).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  54. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031, doi: nature05414 (2006).

    ADS  Article  PubMed  Google Scholar 

  55. Pastorelli, L., De Salvo, C., Mercado, J. R., Vecchi, M. & Pizarro, T. T. Central role of the gut epithelial barrier in the pathogenesis of chronic intestinal inflammation: lessons learned from animal models and human genetics. Front Immunol 4, 280, doi: 10.3389/fimmu.2013.00280 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Mutlu, E. A. et al. Particulate matter air pollution causes oxidant-mediated increase in gut permeability in mice. Part Fibre Toxicol 8, 19, doi: 10.1186/1743-8977-8-19 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. Misra, C., Kim, S., Shen, S. & Sioutas, C. A high flow rate, very low pressure drop impactor for inertial separation of ultrafine from accumulation mode particles. J Aerosol Sci 33, 735–752 (2002).

    CAS  ADS  Article  Google Scholar 

  58. Verma, V. et al. Physicochemical and toxicological profiles of particulate matter in Los Angeles during the October 2007 southern California wildfires. Environ Sci Technol 43, 954–960 (2009).

    CAS  ADS  Article  PubMed  Google Scholar 

  59. Turpin, B. J. & Lim, H. J. Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci Tech 35, 602–610, doi: 10.1080/02786820152051454 (2001).

    CAS  ADS  Article  Google Scholar 

  60. Daher, N., Hasheminassaba, S., Shafer, M. M., Schauer, J. J. & Sioutas, C. Seasonal and spatial variability in chemical composition and mass closure of ambient ultrafine particles in the megacity of Los Angeles. Environ Sci Process Impacts 15, 283–295 (2013).

    CAS  Article  PubMed  Google Scholar 

  61. Morgan, T. E. et al. Glutamatergic neurons in rodent models respond to nanoscale particulate urban air pollutants in vivo and in vitro . Environ Health Perspect 119, 1003–1009, doi: 10.1289/ehp.1002973 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Saffari, A., Daher, N., Shafer, M. M., Schauer, J. J. & Sioutas, C. Global perspective on the oxidative potential of airborne particulate matter: a synthesis of research findings. Environ Sci Technol 48, 7576–7583, doi: 10.1021/es500937x (2014).

    CAS  ADS  Article  PubMed  Google Scholar 

  63. Hansen, C. S. et al. Diesel exhaust particles induce endothelial dysfunction in apoE−/− mice. Toxicol Appl Pharmacol 219, 24–32, doi: S0041-008X(06)00402-9 (2007).

    CAS  Article  PubMed  Google Scholar 

  64. Navab, M. et al. D-4F-mediated reduction in metabolites of arachidonic and linoleic acids in the small intestine is associated with decreased inflammation in low-density lipoprotein receptor-null mice. J Lipid Res 53, 437–445, doi: jlr.M023523 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. Burns, K. A. et al. Role of estrogen receptor signaling required for endometriosis-like lesion establishment in a mouse model. Endocrinology 153, 3960–3971, doi: 10.1210/en.2012-1294 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72, 5069–5072, doi: 72/7/5069 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci 374, doi: 10.1098/rsta.2015.0202 (2016).

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The authors would like to express gratitude to John Vu for assistance in Luminex assay and Dr. Tyler Beebe for proofreading of the manuscript. This project was supported by the National Heart Lung and Blood Institute, R01HL083015 (TKH), R01HL111437 (TKH), R01HL129727 (TKH), R01HL118650 (THK), EB U54 EB0220002 (THK), P02 HL030568 (AMF, SR, MN), a Leducq Foundation Network grant (AMF, SR, MN), and by the Southern California Particle Center, funded by EPA under STAR program (CS) and the South Coast Air Quality Management District Award (CS), VA Merit Review: Project F-7219R (JP, ZL), VA Cooperative Study CSP 577 Confirm Trial (JP), NIDDK CURE:DDRC NIH P30 DK 41301 (JP), VA Merit Review (ZL), NIDDK K01 DK088937 (ML) and NIEHS R01ES014639 (MJC).

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Authors and Affiliations



R.L. designed and performed the experiments, analyzed the data and wrote the manuscript. J.Y. and S.H. performed microbiota and cecal lipid analysis, analyzed the data and revised the manuscript. A.S. prepared UFP samples, conducted chemical analysis of the UFP samples and revised the manuscript. J.J. performed microbiota analysis, reviewed and revised the manuscript. K.B. performed exposure procedure and tissue collection. G.H. did plasma and intestinal lipid analysis. M.L. and N.M. conducted gut permeability assays and revised the manuscript. J.M. organized and analyzed microbiota data. N.J. helped tissue collection, analyzed the data and revised the manuscript. B.Z. and H.K. helped in vitro permeability assay and characterization of the UFP samples. M.C., S.R., Z.L., J.P., A.F., C.S. and M.N. critically reviewed data and conclusions and revised the manuscript to the final version. T.H. conceived the study, analyzed the data, critically review and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tzung K. Hsiai.

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The authors declare no competing financial interests.

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Li, R., Yang, J., Saffari, A. et al. Ambient Ultrafine Particle Ingestion Alters Gut Microbiota in Association with Increased Atherogenic Lipid Metabolites. Sci Rep 7, 42906 (2017).

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