Animals and whole-body inhalation exposure to concentrated ambient PM2.5 (CAP)
3-week-old male C57Bl/6 J mice were purchased from the Animal Center of Shanghai Medical School, Fudan University (Shanghai, China). After 1-week acclimation, mice were subjected to exposure to FA (n = 10) or CAP (n = 10) using a versatile aerosol concentration enrichment system (VACES) that was modified for long-term whole-body exposures as previously described [15, 16]. The VACES was located on the campus of School of Public Health, Fudan University (130 Dong’an Road, Xuhui, Shanghai, China). The exposures were performed from March 2016 to March 2017. The exposure protocol comprised exposures for 8 h/day, 6 days/week (no exposure on Sunday). Ambient PM2.5 and CAP were collected weekly throughout the whole duration of exposure, and their elemental composition was determined by inductively coupled plasma mass spectroscopy (ICP-MS) for trace element analysis as previously described [17, 18]. During the period of exposure, mice were kept on a 12-h light/dark cycle at room temperature (20–25 °C) and 40–70% relative humidity, and received water and standard food ad lib. All procedures in the present study were approved by the institutional animal care and use committees of Fudan University, and all the animals were treated humanely and with regard for alleviation of suffering.
Intraperitoneal glucose tolerance test (IPGTT)
After 46-week-exposure to FA/CAP, mice were subjected to IPGTT on that Sunday. Before testing, mice (50 weeks old) were fasted (initiated immediately after the Saturday’s exposure) for 16 h. On the day of experiments, the basal glucose level of tail vein blood was determined using an automatic glucometer (Glucotrend 2, Roche Diagnostics), and then mice were intraperitoneally injected with glucose (2 g/kg body weight). The glucose levels of the tail vein blood at 15, 30, 60, and 120 min after injection was measured as described above.
Insulin tolerance test (ITT)
ITT was performed on mice after the 47-week-exposure to FA/CAP on that Sunday. Before testing, mice (51 weeks old) were fasted for 4 h. The basal glucose level of tail vein blood was determined using an automatic glucometer (Glucotrend 2, Roche Diagnostics) and then mice were intraperitoneally injected with insulin (0.5 U/kg body weight). The glucose levels of the tail vein blood at 15, 30, 60, and 120 min after injection was measured as described above.
Faecal sample collection
After 48-week-exposure to FA/CAP, mice (52 weeks old) were immediately transferred to empty autoclaved metabolism cage (individually housed, no bedding), and allowed to defecate normally. The 24-h faecal pellets of each mouse were collected and stored in empty autoclaved 1.5 ml Eppendorf tubes using a sterile toothpick. The faecal samples were immediately placed on dry ice and then transferred to − 80 °C freezer. Samples were stored at − 80 °C until ready to extract DNA.
DNA extraction and sequencing
The total genomic DNAs of the faecal samples were extracted using a MoBio PowerFecal DNA extraction kit (Qiagen) as per the manufacturer’s instructions. Briefly, the samples were homogenized in a 2 ml bead beating tube containing garnet beads. The lysis of host cells and microbial cells was facilitated by both mechanical collisions between beads and chemical disruption of cell membranes, ensuring efficient extraction from even the toughest microorganisms. The total genomic DNA was captured on a silica spin column and then eluted with 50 μl of elution buffer from the column after washing with washing buffer. These genomic DNA samples were stored at − 80 °C until the preparation of sequencing libraries. To prepare the sequencing libraries, the DNA concentration and quality was determined with a NanoDrop 1000 spectrophotometer (Thermo Scientific) and by agarose gel electrophoresis (1% wt/vol agarose in tris-acetate-EDTA buffer), respectively. One sample in FA group failed in this quality control (low DNA concentration), and therefore 9 FA and 10 CAP samples were subjected to library preparation and sequencing. The bacterial 16S rRNA gene V4 region and fungal ITS1 region were amplified by PCR using primers as follows: the bacterial community [19]: barcoded 515F (5′-GTG CCA GCM GCC GCG G-3′) and the reverse primer 907R (5’-CCG TCA ATT CM TTT RAG TTT-3′); the fungal community [20]: ITS1F (5’-CTT GGT CAT TTA GAG GAA GTA A-3′) and ITS2R (5’-GCT GCG TTC TTC ATC GAT GC-3′). Each 20 μl PCR reaction mix included 4 μl of 5× FastPfu buffer, 2 μl of 2.5 mM dNTPs, 0.8 μl of forward primer (5 μM), 0.8 μl of reverse primer (5 μM), 0.4 μl of FastPfu polymerase, 10 ng of template DNA, and ddH2O was added to make up the final volume to 20 μl. Thermal cycling was performed in a 9700 PCR System (ABI, GeneAmp 9700) with the following cycling: initial denaturation at 95 °C for 5 min followed by 27 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min. All PCR products were subjected to agarose gel electrophoresis (2%) followed by purification using the AXYGEN gel extraction kit (Axygen). The purified amplicons were quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher) and QuantiFluor™-ST Blue-florescence quantitative system (Promega). They were then sequenced using the Illumina MiSeq system (Illumina) as per the manufacturer’s guidelines.
Bioinformatics analysis
The primary sequencing data were saved in the Fastq format at SRA (Sequence Archive, http://www.ncbi.nlm.nih.gov/Traces/sra). All pyrosequencing reads were pre-processed based on the barcode and primer-end readers (PE readers) using Usearch software (version 7.1, http://drive5.com/uparse/). All reads recruited to the following analyses had the barcode, a minimal average quality score of 20, and maximally 2 mismatches within the primers. Additionally, all overlapped reads (a minimal overlap of 10 bp that had a mismatched rate of ≤0.2.) were merged. To avoid unnecessary computations, the repetitive sequences were extracted and discarded (http://drive5.com/usearch/manual/singletons.html). The optimized sequences were then clustered into operational taxonomics units (OTUs) using UCLUST followed by de novo OTU picking, and chimeras were removed using RDP gold database on Usearch software (version 7.1, http://drive5.com/uparse/). The bacterial and fungal taxonomy was assigned using Naïve Bayesian classifier in QIIME platform [21, 22] using SILVA database (Release 119, http://www.arb-silva.de) [23] and Unite fungal database (Release 6, http://unite.ut.ee/index.php) [24], respectively.
Microbial diversity and richness analysis
OTU-based alpha diversity was estimated using four matrices of Mothur (www.mothur.org /wiki/Schloss_SOP#Alpha_diversity, version v.1.30): ACE, Chao-1, Shannon and Simpson. While ACE and Chao-1 estimators are used to reflect the total number of species in a sample, known as the richness of the community, Simpson and Shannon estimators are quantitative indicators of biodiversity in a region. The calculation of Simpson and Shannon estimators are based on different algorithms, and a larger Simpson estimator or a smaller Shannon estimator represents a lower community diversity. UniFrac distance analysis and principal co-ordinates analysis (Pcoa) using the relative abundance of OTUs were performed to estimate the beta diversity of community. In addition, linear discriminant effect size (LEfSe) analysis was used to find features differentially represented between the groups: a nonparametric factorial Kruskal-Wallis sum-rank test was used to detect significantly (p < 0.05) differential taxa, and the identified taxa were further subjected to a linear discriminant analysis (LDA) to evaluate the effect size of each single differential taxon.
Mediation analysis
The mediation analysis was performed to evaluate the contribution of alteration in gut microbiota to CAP exposure-induced abnormal glucose metabolism. The total effect of CAP exposure on abnormality in glucose homeostasis (X) was assumed to be decomposed into a direct effect (Y) and an indirect effect (M) that is mediated by alteration in gut microbiota. [25] The mediation was then calculated based on two linear mixed effect (LME) models as demonstrated below [26]:
$$ {M}_i={\beta}_0+\alpha {X}_i+{\varepsilon}_i $$
$$ {Y}_i={\beta}_0^{\prime }+\lambda {M}_i+\theta {X}_i+{\eta}_i $$
Here i denotes subject (CAP exposure in the present study). β0 and \( {\beta}_0^{\prime } \) are the intercepts for M and Y, respectively. The effect of X on M is designated as α, the effect of M on Y is designated as λ, and the direct effect of X on Y is designated as θ. ε
i
and η
i
are residuals for M and Y, respectively. The mediation analysis was conducted using R software (version 2.4.2, mediation package).
Statistical analysis
All data were presented as mean ± SEM if not specified. Statistical significances were evaluated by student’s t test or ANOVA analysis (with Bonferroni post-test) using GraphPad Prism Software (version 5), and a p < 0.05 was set as a significance. The area under curve (AUC) for each mouse’s IPGTT and ITT data were calculated using GraphPad Prism Software (version 5. Ybaseline = 0, all peaks must go above the baseline, and ignore peaks that are less than 10% of the distance from minimum to maximum Y). The Spearman rank correlation and the Pearson correlation analyses were performed using GraphPad Prism Software (version 5).