Nanoparticle characterization.
The titanium dioxide nanoparticles (TiO2 NPs) were purchased from Shanghai Macklin Reagent Co. Ltd, China. The size and shape of the particles was characterized by scanning electron microscopy (SEM, Nova, Tecnai F30, FEI Company, Oregon, USA). Energy dispersive X-ray spectroscopy (EDS, Nova_NanoSEM430, FEI Company, Oregon, USA) was used to measure the ratio of Ti to O atoms. The purity of the particles was analyzed by detecting the content of Ti element using inductively coupled plasma mass spectrometry (ICP-MS, IRIS Advantag, TJA, Franklin, MA, USA). The crystal structure of the particles was identified by X-ray powder diffractometry (XRD, PANalytical’s X’Pert PRO, X’Celerator, EA Almelo, Netherlands). The specific surface area (SSA) of the particles was measured according to Brunauer–Emmett–Teller (BET) method (Quantachrome, Autosorb 1, Boynton, FL, USA).
The artificial gastric juice (AGJ, pH 1.2) was prepared using 10 g/L pepsin (3800 units/mg) and 45 mmol/L HCl. The artificial intestinal juice (AIJ; pH 6.8) was made with 10 g/L trypsin (2500 units/mg) and 6.8 g/L KH2PO4. The pH was adjusted to 6.8 using 0.1 mol/L NaOH. After the TiO2 NPs and glucose were dispersed in ultrapure water (H2O), AGJ or AIJ to obtain a final concentration of 1 mg/mL TiO2 NPs, the suspensions were supersonicated for 15 minutes to break up aggregates. The particle hydrodynamic diameters and Zeta potentials were tested using the ZetaSizer Nano ZS90 (Malvern Instruments Ltd, Malvern, UK).
Animal and experimental design.
Three-week-old healthy Sprague-Dawley rats were bred and supplied by the Department of Laboratory Animal Science, Peking University Health Science Center. The rats were fed a commercial pellet diet and deionized water ad libitum, and kept in plastic cages at 20 ± 2 ℃ and 50–70% relative humidity with a 12:12 h light-dark cycle. After one week of acclimation, rats were weighed and randomized into experimental and control groups, with 6 male rats in each group.
All experimental rats were provided humane care of the animals. The study was conducted in accordance with the Guiding Principles in the European Union Directive 2010/63/EU for animal experiments, and received approval from the Peking University Institutional Review Board.
Suspensions of TiO2 NPs (0, 2, 10, 50 mgkg− 1 BW) were administered to rats via oral gavage in a volume of 1 mL daily for 90 consecutive days, using new and fresh suspensions of TiO2 NPs every day. The basis for dose selection can refer to our previous article [39]. Before each treatment of animals, the TiO2 NPs were dispersed in ultrapure water and sonicated for 15 min and vortexed for 5 min to maintain uniformity. The symptom and mortality were observed and recorded daily throughout the duration of exposure up to 90 days. The body weight of rats was assessed every 7 days and the food intake of rats was recorded every 3–4 days. After 90 days, animals were weighed and sacrificed. The blood samples were collected from the abdominal aorta. Serum was harvested by centrifuging blood at 3000 rpm (1500 g) for 10 min. The liver tissues and feces were also harvested at the end of the experiment.
Sample preparation of metabolomic analysis.
30.0 mg of liver tissue or vacuum-dried feces was added to 900 µl pre-cooled methanol/water (1:1) solution. Then it was homogenized at 30 000 rpm on ice for 30 s (Homogenized for 10 seconds, cooled for 30 seconds, repeated three times) and blended by a vortex for 20 seconds and stored at -20 ℃ overnight. And then, the homogenate was centrifuged (16 000 g, 4 C) for 10 minutes and the supernatant was taken. The supernatant was dried and concentrated in a low temperature vacuum concentrator for 4 hours. Re-suspension was carried out by 200 µl methanol/water (1:1) solvent before analysis. Meanwhile, 20 µl of each sample was taken and then divided into three parts after gentle mixing as quality control (Qc) samples, which were prepared for technical repetition to evaluate the stability and repeatability of the experimental instruments and methods.
100 µl serum samples were added to 400 µl pre-cooled chloroform/methanol (2:1) solution. After vortex mixing, use a shaker (Mixmate, Eppendorf, Germany) to further shake for 15 min at 1200 Hz/min. Then it was centrifuged (12 000 rpm, 4 ℃) for 15 minutes and the lower lipophilic solution was taken. The solution was dried and concentrated in a low temperature vacuum concentrator for 4 hours. Re-suspension was carried out by 50 µl acetonitrile/water (1:1) solvent before analysis. Meanwhile, 5 µl of each sample was taken and then divided into three parts after gentle mixing as quality control (Qc) samples.
Untargeted metabolomics analysis using HPLC-MS.
Ultra High Performance Liquid Chromatography-Q-Exactive Orbitrap-High-resolution Mass Spectrometry System (UPLC-QEMS, U3000, Thermo, USA) was used for untargeted metabolomics analysis. The samples were randomly injected after disruption of the order to control the possible impact of instrumental stability fluctuation. Three Qc samples were analyzed before experimental samples, after half of all samples and after all samples, respectively. The QEMS was equipped with an electrospray ionization source (ESI). Fragmentation was achieved by high-energy collision dissociation (HCD). The normalized collision energies were 15, 30 and 45 eV, respectively. The results were measured by positive ion mode and negative ion mode. The mass scanning range was 50-1100 m/z, and the total scanning resolution of parent ions (MS) was 60 K.
Analysis of metabolomic data.
The peaks in the mass spectrum data were collected. The metabolites were identified after relative standard deviation de-noising in the mode of positive and negative ion modes respectively. Then, the missing values were filled up by half of the minimum value. Also, total ion current normalization method was employed in this data analysis. The final dataset containing the information of peak number, sample name and normalized peak area was imported to SIMCA15.0.2 software package (Sartorius Stedim Data Analytics AB, Umea, Sweden) for multivariate analysis. Data was scaled and logarithmic transformed to minimize the impact of both noise and high variance of the variables. After these transformations, PCA (principle component analysis, PCA), an unsupervised analysis that reduces the dimension of the data, was carried out to visualize the distribution and the grouping of the samples. 95% confidence interval in the PCA score plot was used as the threshold to identify potential outliers in the dataset.
In order to visualize group separation and find significantly changed metabolites, supervised orthogonal projections to latent structures- discriminate analysis (OPLS-DA) was applied. Then, a 7-fold cross validation was performed to calculate the value of R2 and Q2. R2 indicates how well the variation of a variable is explained and Q2 means how well a variable could be predicted. To check the robustness and predictive ability of the OPLS-DA model, a 200 times permutations was further conducted. Afterward, the R2 and Q2 intercept values were obtained. Here, the intercept value of Q2 represents the robustness of the model, the risk of overfitting and the reliability of the model, which will be the smaller the better. Furthermore, the value of variable importance in the projection (VIP) of the first principal component in OPLS-DA analysis was obtained. It summarizes the contribution of each variable to the model. The metabolites with VIP > 1 and p < 0.05 (student t test) were considered as significantly changed metabolites. In addition, commercial databases including KEGG (Kyoto Encyclopedia of Genes and Genomes) (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/) were used for pathway enrichment analysis.
Measurement of lipid peroxidation.
Malondialdehyde (MDA), a typical lipid peroxidation product, was measured in serum using the Lipid Peroxidation (MDA) Colorimetric Assay Kit (Beijing Leagene Biotechnology Inc, Beijing, China). The MDA in the sample is reacted with Thiobarbituric Acid (TBA) to generate the MDA-TBA adduct. The MDA-TBA adduct can be easily quantified colorimetrically at 532 nm.
Absolute quantitative lipidomics.
With reference to the method of Tu et al[65]., this study applied absolute quantitative lipidomics to verify the key lipid metabolites. 10 µL of the sample was mixed with 190 µL water, and then 480 µL extract solution (Methyl tert butyl ether (MTBE): methanol (MeOH) = 5:1) containing internal standard was added. After vortex, sonication, and centrifugation (3000 rpm for 15 min at 4 ℃), the supernatants were combined and dried in a vacuum concentrator at 4 ℃. Then, the dried samples were reconstituted in 100 µL of resuspension buffer (Dichloromethane (DCM): MeOH = 1:1) by sonication on ice for 10 min. The constitution was then centrifuged at 12000 rpm for 15 min at 4 ℃, and 30 µL of supernatant was transferred to a fresh glass vial for LC/MS analysis. The UHPLC separation was carried out using a SCIEX ExionLC series UHPLC system equipped with a ACQUITY UPLC HSS T3 1.8 µm 2.1*100 mm column. The mobile phase A consisted of 40% water, 60% acetonitrile, and 10 mmol/L ammonium formate. The mobile phase B consisted of 10% acetonitrile and 90% isopropanol, and 10 mmol/L ammonium formate. The column temperature was 40 ℃. The auto-sampler temperature was 6 ℃, and the injection volume was 2 µL. AB Sciex QTrap 6500 + mass spectrometer was applied for assay development. Typical ion source parameters were: IonSpray Voltage: +5500/-4500 V, Curtain Gas: 40 psi, Temperature: 350℃, Ion Source Gas 1:50 psi, Ion Source Gas 2: 50 psi. Skyline 20.1 Software was employed for the quantification of the target compounds. The absolute content of individual lipids corresponding to the internal standard (IS) was calculated on the basis of peaks area and actual concentration of the identical lipid class IS.
Statistical analysis.
Methods of statistical analysis for data of metabolomics were described above. Other data were expressed as means ± SD and analyzed with SPSS 20.0. When the homogeneity of variance is satisfied, One-way variance (ANOVA) with LSD test was applied to evaluate the statistical significance of differences between the experimental groups and the control. When not satisfied, the differences among groups were compared by nonparametric Kruskal Wallis method with Steel Dwass test. A p value of less than 0.05 was considered to be statistically significant.