Chronic Exposure to Ambient Particulate Matter Induces Gut Microbial Dysbiosis in a Rat COPD Model

doi:10.21203/rs.3.rs-19903/v2

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Chronic Exposure to Ambient Particulate Matter Induces Gut Microbial Dysbiosis in a Rat COPD Model

https://doi.org/10.21203/rs.3.rs-19903/v2

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published 19 Oct, 2020

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Background: The role of the gut microbiota in the pathogenesis of chronic obstructive pulmonary disease following exposure to ambient particulate matter is largely unknown. We hypothesized that exposure alters gut microbial composition and metabolites and may involved in the pathogenesis of chronic obstructive pulmonary disease.

Methods: Fifty-four male Sprague-Dawley rats were exposed to clean air, biomass fuel, or motor vehicle exhaust for 4, 12, and 24 weeks. Lung tissue was assessed histologically and gut microbial composition was assessed by 16S rRNA pyrosequencing. Serum lipopolysaccharide levels were measured and short-chain fatty acids in colon contents were quantified.

Results: After a 24-week exposure to particulate matter, rats exhibited pulmonary inflammation and pathological changes characteristic of chronic obstructive pulmonary disease. The gut microbiome was characterized by decreased microbial richness and diversity, distinct overall microbial composition, lower levels of short-chain fatty acids, and higher serum lipopolysaccharide.

Conclusion: Chronic exposure to ambient particulate matter induces gut microbial dysbiosis and metabolite shifts in a rat model of chronic obstructive pulmonary disease.

Pulmonology

Biomass fuel

motor vehicle exhaust

COPD

gut microbiome

Air pollution is a primary environmental cause of chronic respiratory diseases. Industrial activities, vehicular emissions, and household biomass combustion are major sources of ambient particulate matter (PM) [1, 2]. Incomplete combustion of biomass fuel and living in proximity to traffic have been associated with a high prevalence of chronic obstructive pulmonary disease (COPD) [3,4], while reductions in ambient PM have been shown to decrease the risk of COPD [5]. Therefore, it is important to elucidate the mechanism of ambient PM-induced COPD.

In a previous study, we established a rat COPD model via exposure to biomass fuel (BMF) and motor vehicle exhaust (MVE), which can be used to investigate COPD in non-smokers [6]. We found that both BMF and MVE exposure induced airway cells to secrete cytokines that caused pronounced COPD in rats. However, COPD is a complex disease with multiple sub-phenotypes that affects not only the lungs, but also the gastrointestinal tract [7], and its mechanisms are not completely understood. Over the past few years, emerging evidence has suggested that dysbiosis of the gut microbiome may be involved in the pathogenesis of various respiratory diseases [8,9], but the effects of the gut microbiota on COPD following chronic exposure to ambient PM are largely unknown.

Gut exposure to air pollutants occurs via mucociliary transport of PM from the respiratory tract and by ingestion of food and water [10,11], suggesting that the steady-state concentrations of PM in the gut may mirror concentrations in the lung. We hypothesized that chronic exposure to ambient particulate matter induces gut microbial dysbiosis in a rat COPD model. To test our hypothesis, we established a rat COPD model using exposures to BMF and MVE. Rats were exposed for 4, 12, and 24 weeks, and changes in pulmonary histopathology, microbial composition, and microbial metabolites (lipopolysaccharide [LPS] and short-chain fatty acids [SCFAs]) were measured.

Animals

Male Sprague-Dawley rats (7–9 weeks in age) were purchased from Guangzhou University of Chinese Medicine (Guangzhou, China). The Animal Medical Center of Guangzhou Medical University reviewed and approved all experiments (identification number: GY2019-009). A total of 54 rats were randomly divided into three groups: control, BMF, and MVE (n = 6 per group) for three exposure durations (4, 12, and 24 weeks). All rats were kept in a specific pathogen-free room and were housed three to a cage. Except the time of the PM exposure, the animal facility conditions of the BMF and MVE groups are the same as those of the control group. The animal facility maintained temperature and relative humidity at 23 ± 2°C and 40%–70%, respectively. Lighting was artificial with a sequence of 12 h light (06:00–18:00) and 12 h dark. Commercially available rodent food pellets and water were provided ad libitum. Rats were weighed every 2 weeks throughout the study. Corncob bedding and cage are replaced every 3 days. The rats were observed for any sign of illness a minimum of twice daily.

PM exposure system and characterization of the test atmosphere

Fig. 1Adepicts the design of the study. Rats were exposed to PM as described previously [6]. Briefly, all animals were exposed in whole-body inhalation chambers for 4 h/day, 5 days/week either 4, 12, or 24 weeks. PM mass concentrations, particle size distributions, and gas concentrations were monitored each day of exposure. DustTrak II aerosol monitors (model 8530, TSI, Shoreview, MN, USA) were used to monitored PM mass concentrations and particle size distributions. Testo 340 portable flue gas analyzers (Testo, Lenzkirch, Germany) were used to monitored gas concentrations (O₂, carbon monoxide, nitrogen oxides, and sulfur dioxide) in the exposure rooms. The control group was exposed to clean air.

Exposure to BMF. We used China fir sawdust (40 g/per exposure) to produce BMF smoke, which was sent into the animal exposure room through a piston pump (5 L/min). Rats were exposed to BMF smoke for four 1-h periods, 5 days per week. The test atmosphere was measured during the first hour.

Exposure to MVE. MVE was produced by a Wuyang model WY48QT-2, 1.6-Kw, 125-cm³, one-cylinder, four-cycle, gasoline-powered motorcycle (Guangzhou, China). Premium low-sulfur gasoline (<150 ppm; Petro Inc., El Paso, TX, USA) was used to produce MVE. Prior to the exposure session, the motorcycle engine was operated in an idle state for 2 min to produce sufficient MVE. Rats were exposed for two 2-h periods, 5 days per week. The test atmosphere was measured during the first 2 h.

Ambient BMF and MVE samples were collected throughout the duration of exposure, and concentrations of organic carbon, elemental carbon, polycyclic aromatic hydrocarbons, and metals were measured at the Guangzhou Institute of Chemistry, Chinese Academy of Sciences (Guangzhou, China) according to a previous method [12, 13].

Measurement of lung function

Spirometry data were obtained as previously described using a Forced Pulmonary Maneuver System (Buxco Research Systems, Wilmington, NC, USA) [6]. Rat were sedated with 3% pentobarbital (1mL/Kg), and were tracheostomized and intubated, then the mice placed supine in the body chamber and connected to the system. According to the procedures, the FRC (functional residual capacity), FEV20 (forced expiratory volume in 20 seconds), FEV100 (forced expiratory volume in 100 seconds) and PEF (peak expiratory flow) were measure. At least three acceptable maneuvers for each test of every mice were conducted to obtain a reliable mean spirometry data.

Sample preparation

Rats were sacrificed by CO₂ after 4, 12, and 24 weeks of exposure (on days 29, 85, and 169, respectively). Blood samples were collected from the heart and centrifuged at 1,700 × g for 15 min at 4°C. Serum was stored at −80°C. Proximal colon contents were harvested using sterile instruments for each individual animal and site. Fresh proximal colon contents samples were snap-frozen in liquid nitrogen then stored at −80°C for microbial and SCFA analysis.

Bronchoalveolar lavage fluid differential cell count

Bronchoalveolar lavage fluid was collected as previously reported [14]. Cells were isolated by centrifugation at 300 × g for 10 min at 4°C and stained with Diff-Quik stain (Baso Diagnostics, Zhuhai, China). Differential cell counts were assessed from 400 cells counted on each slide.

Lung morphometric analysis

As described previously [6], lung tissues were fixed with 4% paraformaldehyde solution and embedded in paraffin using standard methods. Sectioning and staining were performed by the Pathology Center of the First Affiliated Hospital of Guangzhou Medical University (Guangzhou, China). All slides were scanned and analyzed using an image analyzer platform (Leica, Wetzlar, Germany). Alveolar enlargement and destruction, and the bronchial wall thickness was calculated as describe previous [15].

Serum Levels of Lipopolysaccharide and total BALF protein assay

Serum levels of LPS were measured using a commercial chromogenic end-point TAL kit (Xiamen Bioendo Technology Co., Ltd., Xiamen, China). All procedures were performed according to the manufacturer’s instructions. The total protein in the BALF determined by Bicinchoninic Acid (BCA) method using a commercial BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, USA).

Microbiota analyses

DNA was isolated from colon contents using a Qiagen QIAamp® DNA extraction kit (Qiagen, Hilden, Germany) according to the manufacturer’s recommendations. 16S rRNA gene amplification, in vitro transcription, and labeling and hybridization were performed following the Illumina 16S Metagenomic Sequencing Library Preparation guide [16]. We used a MiSeq rRNA amplicon sequencing protocol to PCR-amplify the V3–V4 variable regions (amplicon size expected: approximately 460 bp). 16S amplicon PCR forward primer was 5′-(TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG)-3′ and 16S amplicon PCR reverse primer was 5′-(GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C)-3′ [17]. All samples were paired-end sequenced on an Illumina MiSeq PE250 platform (San Diego, CA, USA) by the RiboBio Genome Center (Guangzhou, China). 16S rRNA gene sequence analysis, including raw sequence filtering and taxonomic classification, was performed as described previously [18]. The bar diagrams of alpha diversity indices and relative abundance were drawn using GraphPad Prism 8 software (La Jolla, CA, USA).

Quantification of SCFAs in colon contents

Seven SCFAs (acetic, propionic, butyric, isobutyric, valeric, isovaleric, and caproic acids) in the colon were measured by high-performance gas chromatography-mass spectrometry (Agilent 6890N; Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer’s recommendations. Briefly, a total of 100 mg of the colon contents were homogenized by ultrasonication in 600-μL reactions containing 100 μL of phosphoric acid (15%), 100 μL of 4-methylvaleric acid (250 μg/mL), and 400 μL of diethyl ether. The mixtures were vortexed and centrifuged until a clear supernatant was obtained. The sample was subjected to a high-performance liquid chromatography column (Agilent Technologies) for analysis of SCFAs.

Statistical analysis

Data are reported as mean ± standard deviation (SD). Comparisons were performed using ANOVA and p values were corrected for multiple testing with the Bonferroni method. Statistical analysis was performed using SPSS version 24 (IBM SPSS, Armonk, NY, USA). Correlations between serum LPS levels and the pulmonary mean linear intercept were assessed using Spearman’s rank correlation. p < 0.05 was considered significant.

Particle size and gas concentrations during PM exposure

Particle size distributions and gas concentrations were measured during PM exposure. Fig. 2 and Table 1 show the concentrations of PM with a diameter ≤10, 2.5, and 1 μm (PM₁₀, PM_2.5, and PM₁) during BMF and MVE exposures. Average concentrations of PM₁₀, PM_2.5, and PM₁ in the BMF exposure were 25.87 ± 2.99 mg/m³, 21.91 ± 1.84 mg/m³, and 19.60 ± 1.76 mg/m³, respectively. In the MVE exposure, average concentrations were 1.86 ± 0.20 mg/m³, 1.85 ± 0.24 mg/m³, and 1.82 ± 0.27 mg/m³, respectively. BMF exposure caused notably higher particulate emissions. To estimate levels of gaseous pollutants generated by combustion, we also measured gas concentrations during both BMF and MVE exposures. Table 1 lists the mean ± SD concentrations of O₂, carbon monoxide, nitrogen oxides, and sulfur dioxide during exposure. Tables 2–4 list the elemental composition of the particulates.

Evaluation of COPD model

Exposure to PM affected body weight and induced pulmonary inflammation. Rats continued to gain weight during PM exposure, with a total weight gain of 162.3% (controls), 135.2% (BMF group), and 130.4% (MVE group). The BMF and MVE groups maintained a steady rate of weight gain throughout the exposure period, but the rate was slower than the rate of weight gain in the controls after 4 weeks (p < 0.01; Fig. 2A). The rats exposed to BMF or MVE had more sediment particulate matter in bronchoalveolar lavage fluid as early as 4 weeks into the exposure period (Fig. 2B). The levels of total protein in BALF were both higher in the MVE group or BMF group than in controls since a 4-week exposure (p < 0.05 or p < 0.01, Fig. 2C). Moreover, a significant increase in total leukocyte counts were observed both in the BMF group and MVE group (p < 0.05 or p < 0.01, Fig. 2D). The total leukocyte counts started to increase greatly since a 4-week exposure and remained elevated to the 24 weeks of exposure. Differential cell counts showing that increase in total leukocyte counts were due mainly to increases in alveolar macrophages and neutrophils counts (p < 0.05 or p < 0.01, Fig. 2E-F), and the alveolar macrophages have the highest percentage in total leukocyte counts.

Exposure to PM also induced COPD-like changes in rat lung. Histological analysis showed that significant increases in the mean linear intercept after 24 weeks of exposure in both the BMF (p < 0.01) and MVE (p < 0.01) groups compared to controls (Fig. 3A). Long-term PM exposure can be damaged the lung parenchyma. After 24 weeks BMF and MVE particles exposure, the thickness of bronchial walls in the lungs of rats was increase greatly to that of controls (Fig. 3B), suggesting that the PM exposures induced airway remodeling. Moreover, after 24-weeks of exposure, the pulmonary function test results of FRC were significantly increased, but the PEF, FEV20/FVC and FEV100 were decreased significantly both in the BMF group and MVE group than from controls (p < 0.01, Fig. 3C). Together, these findings suggest that chronic exposure to BMF and MVE induced COPD-like changes in the rat lung.

Gut microbial diversity

To determine whether chronic exposure to ambient PM induces intestinal microbial shifts, we performed 16S rRNA gene sequencing of colon contents from controls and rats exposed to BMF or MVE after 4, 12, and 24 weeks of exposure. After filtering for low-quality reads, 4,479,175 sequence reads from 54 samples were used for analysis and resulted in 22,000 operational taxonomic units. Comparisons between the three groups showed that intra-individual diversity, as measured by the number of operational taxonomic units, decreased significantly in the MVE group after 24 weeks of exposure (p = 0.062, vs CON group; p = 0.122, vs BMF group, Fig. 4A). Other indices (Chao 1, PD_whole_tree, and the Shannon index) were calculated to estimate the within-sample (alpha) diversity. The Chao 1 (p = 0.090, vs CON group; Fig. 4 B) and PD_whole_tree indices (p = 0.216, vs CON group, Fig. 4 C) were lower in the MVE group after 24 weeks exposure. The reduced richness of operational taxonomic units and alpha diversity in the gut microbiota suggest a possible deficiency in healthy microflora in the BMF and MVE groups.

To investigate the difference between gut microbial communities in the three groups, we analyzed the taxonomical community structure of the microbiome in colon contents. At the phylum level, all samples at all measurement time points from the three groups contained four major bacterial phyla: Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. The first three phyla accounted for over 97% of the total sequences in all three groups (Fig. 5). After a 4-week exposure, the MVE group had a lower relative abundance of Firmicutes (p = 0.003 vs. the control group; p = 0.002 vs. the BMF group) but a higher relative abundance of Proteobacteria (p = 0.099 vs. the control group; p = 0.055 vs. the BMF group, Fig. 5A). After a 12-week exposure, the MVE group exhibited relative abundances of Firmicutes (p = 0.019 vs. the control group; p = 0.072 vs. the BMF group, Fig. 5B) that were consistent with the trend seen after 4 weeks of exposure. However, after 24 weeks of exposure, the three groups exhibited a similar relative abundance of four major bacterial phyla (p > 0.05, Fig. 5C). These findings indicate gut microbiota dysbiosis after PM exposure.

Quantification of microbial metabolites

We analyzed the levels of SCFAs in colon contents following 4- and 24-week exposures using gas chromatography. Acetic, propionic, and caproic acids were detected in all colon contents, but butyric, isobutyric, valeric, and isovaleric acids were not detected in some samples. SCFA levels differed between the control and exposure groups. Levels of total SCFAs were significantly lower in the MVE group than in controls (p = 0.125) after 4 weeks and 24-week exposure (p = 0.041, Fig. 6A). Moreover, a significant decrease in acetic acid was observed in colon contents from the MVE group relative to contents in controls (p = 0.036, Fig. 6B). Propionic and caproic acids exhibited similar levels after PM exposure (Fig. 6C, D).

Serum LPS levels were higher in the MVE group than in controls after a 4-week exposure (p = 0.054). After a 24-week exposure, serum LPS levels were significantly higher in both the BMF (p = 0.007) and MVE (p = 0.007) groups than in controls (Fig. 6E), and exhibited a weak positive correlation with the pulmonary mean linear intercept (r² = 0.4552, p < 0.001; Fig. 6F).

In recent years, the association of ambient PM exposure with COPD has been well-established [19]. In previous work, we found that elevated PM concentrations were associated with increased COPD prevalence and diminished respiratory function [4]. Moreover, use of clean fuels and improved ventilation have been associated with better pulmonary function and a reduced risk of COPD [5]. We then established a rat COPD model using exposure to biomass fuel and motor vehicle exhaust to study the pathophysiology of PM-induced COPD [6], but the underlying biological mechanisms are still unclear. PM research has mostly focused on the lung inflammatory response to inhalation, endothelial dysfunction, and oxidative stress, but the effects of PM on the intestine and its role in the pathogenesis of COPD are largely unknown. In the present study, we demonstrated that chronic exposure to PM induced COPD-like characteristic pathological changes and pulmonary inflammation in rats that were concomitant with a decrease in the abundance and diversity of gut microbiota.

Recent studies have suggested a role for the gut microbiota in the development of respiratory diseases such as asthma, pneumonia, and acute respiratory distress syndrome [9,20,21], although the connection between the lung and the gut is poorly understood. In the present study, we found that PM exposure was associated with decreased abundance and diversity in bacterial populations, similar to the effects reported for fine PM on diabetes and obesity [22-24]. These data from epidemiological and animal studies suggest that long-term exposure to PM causes gut microbiota dysbiosis and may subsequently contribute to increased risk of diabetes and obesity [22,23]. Importantly, a chronic exposure (24 weeks) to PM reduced operational taxonomic units and alpha diversity, consistent with the findings of a study conducted in mice [22]. In addition to diversity, bacterial relative abundance was also affected, and may partially mediate the association of PM with COPD. Notably, the abundance of Firmicutes was first reduced in rats exposed to MVE group, but after 24 weeks of exposure, the relative abundance of Firmicutes increased from 80% to 97%, at the same time that the relative abundance of Firmicutes in rats exposed to BMF decreased from 97% to 88%. Our findings show that gut microbiota dysbiosis in a PM-induced rat model of COPD may depend on exposure duration, the dose, or the sources of PM.

Despite the well-studied pulmonary effects of ambient PM, little is known about its effects on the gastrointestinal tract and microbiome. Ambient PM can enter gastrointestinal tract through the bloodstream or by ingestion, either after clearance via airway cilia or by consumption of contaminated food and water [10,11]. Salim et al. and Beamish et al. proposed a model of PM-induced intestinal and systemic inflammation, suggesting that PM in the gastrointestinal tract causes oxidative stress and increases gut permeability [25-27]. This enables microbial products and PM to penetrate the lamina propria and interact with immune cells. Inflammatory responses in intestinal cells will further alter the micro-environment and enable the growth of particular microbial strains. The altered microbial community and its products affect metabolic processes and thus become a causative factor in disease progression. This imbalance in gut microbiota leads to the migration of bacteria or bacterial products, such as LPS, from the intestinal lumen to mesenteric lymph nodes or the circulation, in association with an inflammatory response [28-30]. LPS is released during bacterial cell division or death and is pro-inflammatory [31, 32]. We found that serum LPS levels increased after both BMF and MVE exposures. LPS causes the production of inflammatory mediators, and the process can result in pathological changes characteristic of COPD in the lung [33].

SCFAs are metabolized by the gut microbiota from otherwise indigestible fiber-rich foods and are involved in metabolism [34]. SCFAs have anti-inflammatory properties, are a source of energy for colonocytes, improve gut barrier function, and reduce intestinal bacterial translocation [35]. A high-fiber diet has been suggested to decrease COPD risk, primarily in current and former smokers [36,37]. Our findings suggest that the decrease in SCFA levels observed following exposure to BMF or MVE. Animal experiments also support this SCFA-related mechanism; an increase in dietary SCFAs was reported to attenuate the development of smoking-related emphysema in rats and mice [38,39]. We propose that SCFAs, and especially acetic acid, may confer a protective effect against PM-induced COPD.

Although the study demonstrates a notable impact of inhaled PM on the gut microbiota and implicates it in the development of COPD, several limitations should be noted. First, in the absence of interventional research, it is difficult to ascertain whether gut microbiota dysbiosis is a cause or a consequence of COPD. This will require the use of germ-free mice and gut microbial transplantation. Second, although exposure to PM was shown to alter the composition and function of gut microbiota, how PM inhalation affects the gut microbiota remains to be determined. More studies are warranted to better understand the pathogenesis of gut microbiota dysbiosis after chronic exposure to ambient PM. Third, we cannot yet ascertain whether PM induction of gut microbiota dysbiosis is time- or dose-dependent.

Chronic exposure to ambient PM decreases the levels of SCFAs in the colon and induces gut microbial shifts and translocation in a rat COPD model.

Ethics approval

All animal care and experiment protocols complied with the guiding principles for the care and use of laboratory animals recommended by the Chinese Association for Laboratory Animal Science Policy. The Animal Medical Center of Guangzhou Medical University reviewed and approved all experiments (identification number: GY2019-009).

Consent for publication

Not applicable.

Availability of data and material

Please contact author for data requests.

Funding

This work was supported by a grant from the National Program on Key Basic Research Projects (2015CB553403), Youth Foundation of the National Key Laboratory of Respiratory Diseases (SKLRD-QN-201908), Science and Technology Program of Guangzhou (201504010018), National Natural Science Foundation of China (81670040, 81570035, 81970045, and 81900030), Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01S155), and 135 Key Research and Development Program (2016YFC1304101).

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

NJL, PXR, YMZ, and BL conceived the study, directed the project, and designed the experiments; THP, BLL, BWH, WTC, ZLF, FH, and JDP obtained the samples, interpreted the results, and analyzed the animal data; NJL and ZWY performed the microbial sequencing analysis and wrote the manuscript; all authors read and approved the final manuscript.

Acknowledgements

We thank Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Author details

¹Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, P.R. China

²Nanhai Hospital Southern Medical University, Foshan 528000, P.R. China

³State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, P.R. China

⁴Department of Respiratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, P.R. China

⁵The School of Basic Medicine, Guangzhou Medical University, Guangzhou 510000, P.R. China

⁶The GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou 510000, P.R. China

BMF, biomass fuel; COPD, chronic obstructive pulmonary disease; FEV20, forced expiratory volume in 20 seconds; FEV100, forced expiratory volume in 100 seconds; FRC, functional residual capacity; LPS, lipopolysaccharide; MVE, motor vehicle exhaust; PEF, peak expiratory flow; PM, particulate matter; SCFA, short-chain fatty acid.

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Table 1. Concentrations of particulate matter (PM) and gaseous pollutants measured during exposure

Test items	CON	BMF	MVE
PM10 (mg/m³)	—	25.87±2.99	1.86±0.20
PM2.5 (mg/m³)	—	21.91±1.84	1.85±0.24
PM1 (mg/m³)	—	19.60±1.76	1.82±0.27
NO₁ (ppm)	—	0.78 ± 0.10	0.31 ± 0.07
NO_X (ppm)	—	0.78 ± 0.10	0.31 ± 0.07
SO₂ (ppm)	—	—	0.82 ± 0.10
CO (ppm)	—	110.1±15.4	129.0±12.6
O₂(%)	20.9 ± 0.04	20.7±0.06	20.4±0.05
Humidity (%)	60.1±5.6	66.0±9.3	73.5±9.9
Temperature (℃)	24.9±0.5	26.1±1.8	27.5±4.4

Values are shown as mean ± SD. CON, control group; BMF, biomass fuel exposure group; MVE, motor vehicle exhaust exposure group.

Table 2. The elemental composition of PAHs in ambient BMF and MVE samples.

PAHs	BMF (mg/kg)	MVE (mg/kg)
Naphthalene	6.95 ± 2.24	—
Acenaphthylene	—	0.03 ± 0.03
Acenaphthene	—	—
Fluorene	—	0.06 ± 0.05
Phenanthrene	2.34 ± 1.07	2.22 ± 1.30
Anthrancene	0.20 ± 0.05	0.07 ± 0.03
Fluoranthene	1.04 ± 0.28	4.17 ± 3.16
Pyrene	0.16 ± 0.13	7.71 ± 5.80
Benzo anthracene	0.22 ± 0.19	2.25 ± 1.24
Chrysene	—	1.05 ± 0.73
Benzo[b+j] fluoranthene	—	4.02 ± 3.90
Benzo[k] fluoranthene	—	0.59 ± 0.38
Benzo[a] pyrene	—	2.19 ± 1.70
Benzo[e] pyrene	—	2.69 ± 1.30
Dibenz[a,h] anthracene	—	—
Benzo[g,h,i] perylene	—	—
Indeno[1,2,3-cd] pyrene	—	4.81 ± 2.75

Results are expressed as mean ± SD; Concentrations were expressed as mg/kg; PAHs, Polycyclic Aromatic Hydrocarbons; Horizontal line mean below detection limit; BMF, biomass fuel exposure group; MVE, motor vehicle exhaust exposure group.

Table 3. The elemental composition of OC/EC in ambient BMF and MVE samples.

OC/EC	BMF (ng/ug)	MVE (ng/ug)
TC	117.24 ± 3.39	153 ± 21.66
OC	116.70 ± 3.31	146.11 ± 19.79
EC	0.54 ± 0.08	6.89 ±1.87
OC/EC	28.10 ± 1.98	10.72 ± 2.11
OC1	67.33 ± 0.63	36.72 ± 7.67
OC2	26.50 ± 1.13	102.09 ± 11.62
OC3	6.86 ± 0.37	4.97 ± 0.38
OC4	1.03 ± 0.18	1.36 ± 0.46
EC1	15.4 ± 1.05	7.79 ±1.48
EC2	0.08 ± 0.02	0.08 ± 0.04
EC3	0.05 ± 0.02	—
OP2	14.99 ± 1.02	0.97 ±0.34

Results are expressed as mean ± SD; OC/EC, organic carbon/elemental carbon; Horizontal line mean below detection limit; BMF, biomass fuel exposure group; MVE, motor vehicle exhaust exposure group.

Table 4. The metal composition in ambient BMF and MVE samples.

Metal	BMF (ng/ug)	MVE (ng/ug)
Na	0.003 ±0.003	0.16 ± 0.09
Mg	0.01 ±0.01	0.11 ± 0.11
Al	—	—
Si	—	0.04 ± 0.03
S	0.15 ± 0.10	1.13 ± 0.26
Cl	0.20 ± 0.15	0.03 ± 0.03
K	0.002 ± 0.001	0.03 ± 0.01
Ca	—	0.52 ± 0.13
Ti	—	0.002 ± 0.002
V	—	—
Cr	—	—
Mn	0.001 ± 0.001	0.002 ± 0.001
Fe	—	0.27 ± 0.10
Co	—	—
Ni	—	—
Cu	—	0.01 ± 0.01
Zn	—	0.30 ± 0.14
Rb	—	—
Sb	0.002 ± 0.001	—
Ba	0.004 ± 0.002	0.02 ± 0.01
Pb	—	0.01 ± 0.01

Results are expressed as mean ± SD; Horizontal line mean below detection limit; BMF, biomass fuel exposure group; MVE, motor vehicle exhaust exposure group.

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Chronic Exposure to Ambient Particulate Matter Induces Gut Microbial Dysbiosis in a Rat COPD Model

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