PM_2.5 exposure aggravates kidney damage by facilitating the lipid metabolism disorder in diabetic mice

Animals

Seven-week-old male C57BL/6 mice and db/db mice were purchased from Changzhou Cavens Laboratory Animals Ltd. All mice were housed five per cage on a 12-hour light/dark cycle and in a climate-controlled environment and had free access to food and water. Animal testing is in accordance with the national guidelines for experimental animal health, and the animal protection and utilization committee of the People’s Hospital of Guizhou Province approved the study [2022 (295)].

PM_2.5 preparation

The PM_2.5 used in the study was purchased from the Standard Reference Materials and was certified by the National Institute of Standards and Technology (SRM2975). PM_2.5 was diluted in phosphate buffered saline (PBS) prior to use.

PM_2.5 exposure

The mice were allowed to acclimatize for 1 week prior to the experiments. C57BL/6 mice (n = 5) comprised the control group (CON) and db/db mice were randomly assigned to 4 groups: no PM_2.5 exposure group (DKD, n = 5), low concentration PM_2.5 exposure group (DKD+L, n = 5), medium concentration PM_2.5 exposure group (DKD+M, n = 5), and high concentration PM_2.5 exposure group (H, n = 5) (Faul et al., 2007). The ear tags of each group of mice were recorded. PM_2.5 exposure dose of mice was 1.8 mg/kg, 5.4 mg/kg, and 16.2 mg/kg, respectively, based on a previous report (Zhang et al., 2017). The mice were exposed to PBS or PM_2.5 by intratracheal instillation thrice a week for 8 weeks.

Sample collection

When the mice reached 16 weeks of age, they were housed individually in metabolic cages to collect 24 h urine samples. They were then euthanized via intraperitoneal injection of sodium pentobarbital. The eyeballs of the mice were extirpated and blood samples were collected. Serum and urine supernatant were collected by centrifugation and stored at −80 °C until analysis. Left kidneys were collected and preserved at −80 °C for a liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis. The right kidneys were used for paraffin section, frozen section and qPCR detection.

Biochemical indicators analysis

The serum blood urea nitrogen (BUN) levels were measured using a Urea Assay Kit (Enzy Chrom, DIUR-500); serum creatinine (SRC) levels were measured using a Creatinine Kit (Nanjing Jiancheng Bioengineering Institute, C011-2-1), urea protein levels were measured using Urea Protein Kit (Nanjing Jiancheng Bioengineering Institute, C035-2-1), serum triglyceride levels were measured using a Triglyceride Assay Kit (ETGA-200; Enzy Chrom). and serum total cholesterol levels were measured with using a Cholesterol Assay Kit (E2CH-100; Enzy Chrom).

Histological analysis

The kidney tissues were postfixed and embedded in paraffin or frozen in an OCT compound. The paraffinized sections were stained with hematoxylin and eosin (HE), Periodic Acid–Schiff (PAS) or immunohistochemical (IHC) stains. HE staining used HE Stain Kit (G1120; Beijing Solarbio, Beijing, China), PAS staining used PAS Stain Kit. For the IHC, the tissues were sectioned and affixed onto coated slides before being subjected to deparaffinization and rehydration. Endogenous peroxidase was blocked by immersing the tissues in 3% H₂O₂ for 10 min. Two additional washes in ddH₂O were performed to remove any remaining after which the sections were then rinsed under tap water. Subsequently, the sections were heated in a pressure cooker, boiled for 2 min and then cooled. The sections were then dried and a hydrophobic pen was used to draw marks around tissues before they were washed with PBS. The sections were incubated in blocking solution (5% bovine serum albumin in PBS) for 30 min. Next, primary antibodies ADRP (15294-1-AP, 1:200; Proteintech, Rosemont, IL, USA), SREBP-1 (14088-1-AP, 1:100; Proteintech, Rosemont, IL, USA), ATGL (55190-1-AP, 1:400; Proteintech, Rosemont, IL, USA), HMGN1 (11695-1-AP,1:200; Proteintech, Rosemont, IL, USA), KIM-1 (ab47635, 1:200; Abcam, Cambridge, UK) diluted in antibody diluent (1% bovine serum albumin in PBS) was added and incubated overnight at 4 °C in a wet chamber. On the next day, the antibody solution was removed and the sections were washed with wash buffer for three times. The sections were incubated with goat anti-mouse (ab6789, 1:2000; Abcam, Cambridge, UK) or goat anti-rabbit (ab7090, 1:300; Abcam, Cambridge, UK) for 45 min at 37 °C, before being rewashed for three times and incubated with DAB, cores were counterstained with hematoxylin, dehydrated and cover slipped. The average optical density value of DAB positive reactant was detected by Image-Pro Plus 6.0 software to reflect the expression of detected protein molecules in renal tissue. Oil Red O staining used the Modified Oil Red O Staining Kit (C0158S; Beyotime, Jiangsu, China). Photographs were captured using a digital slide scanner (GScan-20; Gcell Technologies, Gurgaon, India). The density of DAB was quantified using Image-Pro Plus 6.0 software. The expression levels of the factors in the kidney were quantified using average integrated optical density values in Image-Pro Plus 6.0 software.

Preparing samples for the LC-MS/MS analysis

A total of 100 mg of each kidney sample was accurately weighed. The amount of the sample was placed into a 2 ml centrifuge tube, and 1,000 µL of tissue extract (75% 9:1 methanol: chloroform, 25% H₂O) was added to the 2 ml centrifuge tube. Three steel balls were added, ground at 50 Hz for 60 s in the tissue grinder, and the above procedure was repeated twice; room temperature ultrasound for 30 min, then ice bath for 30 min. After centrifuging for 10 min at 12,000 rpm and 4 °C, the supernatant was transfered to a new 2 ml centrifuge tube, concentrate, and dry. A total of 200 µL 50% acetonitrile solution should be accurately added with 2-Amino-3-(2-chloro- phenyl)-propionic acid (4 ppm) (stored at 4 °C) to redissolve the sample; the supernatant is filtered through a 0.22 µm membrane and transferred to the LC-MS detection bottle (Warren et al., 2017).

Metabolomics analysis based on LC-MS/MS

The data were first converted to mzXML format using MSConvert in the ProteoWizard software package (v3.0.8789) (French et al., 2015) and processed using XCMS (Navarro-Reig et al., 2015) for feature detection, retention time correction and alignment. Metabolite identification was based on mass accuracy (30 ppm) and MS/MS data that matched with databases in HMDB (Wishart et al., 2018) and KEGG. Data normalization was performed using the robust LOESS signal correction (QC-RLSC) (Gagnebin et al., 2017) to correct for systematic bias. After normalization, a maximum of 30% relative standard deviation (RSD) was retained in QC to ensure proper metabolite identification.

For statistical analysis of multivariate data, R language ropes package methods used were PCA, PLS-DA, and OPLS-DA. By using MetaboAnalyst, different metabolites were analyzed according to their pathways (Xia & Wishart, 2011), which combines results from powerful pathway enrichment analysis with the pathway topology analysis. For biological interpretation of higher-level systemic functions, the identified metabolites in metabolomics were mapped to KEGG pathways.

Statistical analysis

The data were analyzed using SPSS 26 and were presented as the mean ± standard error of the mean. The Student’s t-test was used to compare data between the two groups. Comparison among multiple groups and pairwise comparison was performed using one-way ANOVA and LSD test. Correlation analyses were performed using Pearson’s correlation analysis. P < 0.05 indicated statistically-significant results. (^∗ p < 0.05, vs. CON group; ^# p < 0.05, vs. DKD group.)

PM_2.5 exposure aggravates the decline of renal function and dyslipidemia in diabetic mice

The BUN, SRC and urinary albumin-to-creatinine ratio (UACR) were higher in the DKD group than in the CON group. The BUN, SRC and UACR in the DKD+L, DKD+M and DKD+H groups were higher than those in the DKD group, indicating that PM_2.5 exposure made mice more susceptible to severe renal disease (Figs. 1A–1C). The serum triglyceride and serum total cholesterol levels were higher in the DKD group than in the CON group. After PM2.5 exposure the serum triglyceride and serum total cholesterol levels in the DKD+L, DKD+M and DKD+H groups were higher than those in the DKD group, indicating that the PM_2.5 exposure also made the mice more susceptible to hyperlipidemia (Figs. 1D–1E).

PM_2.5 exposure worsens renal lipid metabolism disorder and kidney damage in diabetic mice

The histological analysis of the HE and PAS-stained kidney sections revealed a mild glomerular lesion consisting of mesangial matrix expansions and more extensive vacuolization in proximal tubular cells compared with those in the DKD group, but this was not observed in the CON group. Tubular damage was found to have increased in the DKD+L, DKD+M and DKD+H groups compared to that in both the CON and DKD groups (Fig. 1F). To detect ectopic lipid accumulations in the proximal tubules, Oil Red O staining and IHC of the lipid droplet biomarker ADRP was performed (Heid et al., 1998). The Oil Red O staining and ADRP, analysis revealed more lipid droplet in the DKD+L, DKD+M and DKD+H groups than in the DKD group (Fig. 2A). In DKD mice kidney, the level of SREBP-1 mRNA was increased compared to the CON group, while the level of ATGL mRNA was decreased compared to the CON group. The SREBP-1 mRNA level was higher in the DKD+M and DKD+H group compared to the DKD group, while the ATGL mRNA level was lower in the DKD+M, DKD+H group compared to the DKD group (Figs. 2G, 2H).

The expression of SREBP-1 was significantly higher in the DKD tubules. Notably, the extent of tubular SREBP-1 staining was significantly higher in the DKD+L, DKD+M and DKD+H mice than in the DKD mice and CON (Figs. 2A, 2B). Moreover, the expression of ATGL was decreased in the renal tubules of the DKD mice compared.to that in the CON mice. This alteration was even more marked in the DKD+L, DKD+M and DKD+H groups, which exhibited a significantly lower expression of ATGL expression than the DKD mice (Figs. 2A, 2C).

PM_2.5 exposure aggravates disordered lipid metabolism by upregulating the expression of HMGN1/KIM-1 in diabetic mice

The next analysis investigated whether PM_2.5 exposure causes kidney damage via HMGN1-KIM-1. HMGN1 and KIM-1 were detected by IHC. The levels of HMGN1 and KIM-1 in the tubules increased in the DKD mice compared with those in the CON. Moreover the extent of tubular HMGN1 and KIM-1 staining were significantly higher in DKD+L, DKD+M and DKD+H mice than in DKD (Figs. 3A–3C). There was significant correlation between the protein expression in the kidney KIM-1in the different groups and HMGN1. Positive correlations were detected between HMGN-1 and KIM-1 (r = 0.7459, P < 0.001) (Fig. 3D). The correlations between different groups Oil Red O staining area% and protein expression of ADRP, SREBP-1, ATGL were found to associate significantly with HMGN1. Specifically, positive correlations were detected between KIM-1 and Oil Red O staining area% (r = 0.7830, P < 0.001), ADRP (r = 0.7312, P < 0.001) and SREBP-1 (r = 0.8395, P < 0.001) (Figs. 3E–3G). Specifically, negative correlation was detected between KIM-1 and ATGL (r =  − 0.8079, P < 0.001).

PM_2.5 exposure alters abundant metabolites profiles in the kidneys of diabetic mice

To systematically characterize the metabolic profiles in DKD and the potential metabolic alterations following exposure to PM_2.5 exposure, we performed a non-targeted metabolomic analysis of the s kidney tissue from the DKD and DKD+P groups. In total we identified 474 metabolites from the kidney samples (Table S1).

We constructed an OPLS-DA models for each sample. The models revealed the clustering and separation of the DKD and DKD+P groups (Figs. 4A, 4B; R2Y and Q2 for positive ion mode:0.994, 0.775; R2Y and Q2 for negative ion mode:0.995, 0.819). As demonstrated by the volcano plot, we further identified 50 significantly differentialy expressed metabolites (DEMs) between the DKD and DKD+P groups, based on the screening criteria of VIP > 1 and P < 0.05. Further analysis revealed a significant increase and decrease in the relative levels of 18 and 33 differential metabolites, respectively, after PM_2.5 exposure (Fig. 4C).

We submitted 50 DEMs to Emotionalist 5.0 for a KEGG pathway enrichment analysis. The DEMs from the kidney were significantly enriched in two pathways involving two types of pyrimidine metabolism and starch and sucrose metabolism (P < 0.05 and impact > 0.1; Fig. 4D). There were four DEMs in pyrimidine metabolism and uridine can cause massive ectopic lipid deposition increased in DKD+P (Urasaki, Pizzorno & Le, 2016). Dihydrouracil can improve lipid was downregulated in DKD+P (Sullivan et al., 2022). The expression of metabolites that promote lipid metabolism disorders increased: corticosterone and cortisone after PM_2.5 exposure (Yang & Yu, 2021; Bai et al., 2022), whereas the expression of metabolites that improve lipid metabolism: pantothenic acid, gentisic acid, dihydrouracil, pyridoxine and 3-Hydroxyanthranilate was downregulated in the kidney after PM_2.5 exposure (Sullivan et al., 2022; Kang et al., 2021; Shibata et al., 2013; Danielyan & Simonyan, 2017; Berg et al., 2020). These results are consistent with our biochemical findings and pathological staining detection of PM_2.5 aggravated kidney damage and kidney lipid metabolism disorder. In addition, several metabolites can be linked to oxidative stress and inflammation: pyridoxine, N-Acetylmannosamine, N-Acetylserotonin, and 3-Hydroxymethylglutaric acid (Danielyan & Simonyan, 2017; Gao et al., 2022; Liu et al., 2021; Delgado et al., 2019).