Human subjects
This study was approved by the Institutional Review Board of the Seoul National University (IRB No. 144-2011-07-11) and was performed according to the Helsinki Declaration. Written informed consent was obtained from each participant. A total of 304 individuals from participants enrolled in the Healthy Twin Study in South Korea [35] were selected for this study. Fecal samples from participants were collected at home and immediately frozen in a home freezer, followed by transfer to clinics and storage at -80℃ until further analysis. All participants filled in questionnaires covering lifestyle, medication, disease history, biochemical tests, and anthropometrical measurements. The gut microbiome data were acquired from our previous study (accession number: ERPO10289) [14]. The demographic characteristics of the study subjects are listed in Additional file 9: Table S3.
Measurement of MetS components and definition of MetS
Measurements of waist circumference, blood pressure, triglyceride, HDL cholesterol, and FBS have been previously published [14]. MetS was defined following the revised National Cholesterol Education Program Adult Treatment Panel III criteria with the Korean-specific waist circumference cut-off values for abdominal obesity. The subjects were considered to have MetS if they had three or more of the following five criteria, (1) waist circumference ≥ 90 cm for men or ≥ 85 cm for women, (2) blood pressure ≥ 130/85 mm Hg, (3) triglycerides ≥ 150 mg/dL (1.7 mmol/L), (4) HDL cholesterol < 40 mg/dL (1.03 mmol/L) for men or < 50 mg/dL (1.3 mmol/L) for women, and (5) FBS ≥ 100 mg/dL (5.6 mmol/L).
In vitro batch culture of human fecal microbiota
In vitro colonic fermentation was performed according to Long’s method with minor modification [36]. Briefly, 8 g/L NaCl, 1.15 g/L Na2HPO4, 0.5 g/L L-cysteine, 0.2 g/L KCl, and 0.2 g/L KH2PO4 were dissolved in distilled water and autoclaved for making PBS medium. Fecal samples were obtained from three healthy donors (age 20–30; mean BMI 22.3) who had taken no antibiotics or prebiotics for three months prior to the study. Written informed consent was obtained from donors, and the study was approved by the institutional review board of the Korea Institute of Science and Technology (IRB No. 2015-003). Fecal slurry (10% w/v) was prepared by diluting and suspending the fecal samples with PBS medium in an anaerobic chamber (Coy Laboratory Products Inc., Ann Arbor, MI). The cultivation was started with 5% fecal inoculum by adding 0.9 mL of 10% fecal slurry into 0.9 mL PBS medium (total volume, 20 mL) using a 96-deep well plate. Culturing was performed with various concentrations of Be (3, 30, 300, and 3000 ppb) in an anaerobic jar (MGC, Japan) with AnaeroPack (MGC) at 37 ℃ without stirring. Samples were collected after 24 h and stored in the refrigerator (-20 ℃) for further analysis.
Animals and exposure
All animal experiments were conducted with the approval of the Institutional Animal Care and Use Committee of the Korean Institute of Science and Technology (No. 2018-036) and strictly followed National Institutes of Health guidelines for the use of live animals. Male C57BL/6 mice (5-week-old, 20–22 g; Central Lab. Animal Inc., Seoul, South Korea) were housed in individually ventilated cages at 23 ± 0.5 ℃ and 10% humidity under a 12-h light-dark cycle with ad libitum access to feed and water. All animals were acclimated for 7 days and separated into 2–3 animals/cage, ensuring equal weight average. Be was administered to mice as beryllium sulfate tetrahydrate (Sigma-Aldrich, St. Louis, MO) in drinking water for 50 days. Freshly prepared Be-containing water (3 or 30 ppb) and feed were provided to mice twice a week, while control mice received feed with water alone. Mice were randomly divided into three HFD groups and two ND groups as follows, (1) HFD (45% of total calories from fat; TD.06415; Harlan Laboratories, Indianapolis, IN) with water (n = 8), (2) HFD with 3 ppb of Be water (n = 8), (3) HFD with 30 ppb of Be water (n = 8), (4) ND with water (n = 6), and (5) ND with 30 ppb of Be water (n = 6). Feed and water intake were recorded once a week for each cage and the data were used for the calculation of average intake per mouse per week. The theoretical weekly Be exposure was calculated by multiplying the added Be concentration by the average weekly water intake. Body weight of each mouse was measured once a week and stool samples were collected once a week and immediately stored at -80 ℃ for further analysis. Animals were euthanized by CO2 inhalation at the beginning of the light cycle and after 16 h of food deprivation. Blood samples were collected by cardiac puncture in microtubes containing EDTA and centrifuged at 1,000 × g and 4℃ for 15 min to obtain plasma, and stored at -80 ℃ for subsequent biochemical measurements. The eWAT, cecum, and colon of each mouse were precisely dissected, weighed, and stored for further analysis. All tissues were rinsed with saline and snap-frozen at -80 ℃.
16S rRNA gene sequence analysis
DNA was extracted from stools, in vitro batch culture samples, and mice cecum using a QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) with an additional bead-beating procedure to improve DNA recovery for Gram-positive bacteria. The 16S rRNA genes were amplified using an improved dual-indexing amplification of the V3-V4 region (319F/806R) of the 16S rRNA gene with a heterogeneity spacer [37]. PCR products were purified using AMPure XT beads (Beckman Coulter, Danvers, MA) and quantified using a Qubit dsDNA high-sensitivity reagent (Invitrogen, Carlsbad, CA). Sequencing was conducted on the MiSeq platform using a paired-end 2 × 300-bp reagent kit (Illumina, San Diego, CA). The raw reads were demultiplexed, assembled, and quality-filtered in QIIME 2 (v2018.6) using the default settings. DADA2 was used to filter chimeric reads and artifacts commonly present in Illumina amplicon data [38]. To classify filtered reads to taxonomic groups, a Naive Bayes classifier was trained using the 16S rRNA region (V3-V4), the primer set and read length used (319F/806R, 469 bp), and the Greengenes 99% reference set (v13.8) [39]. This trained feature classifier was then used to assign taxonomy to each read using the default settings in QIIME. Microbial composition at a certain level as well as α- and β-diversity were analyzed using MicrobiomeAnalyst [40]. Non-metric multidimensional scaling (NMDS) plots were generated from a Bray-Curtis distance matrix, and a principal coordinate analysis (PCoA) plot was generated using unweighted Unifrac distances to visually represent microbiota compositional differences among groups. Random forest, a supervised learning method for the classification of human microbiome data [41], was used to select subsets of taxa (genus level) that are highly discriminative of the type of community from Be-exposed mice. We measured feature importance as the mean decrease in model accuracy when that feature’s values were permuted randomly using 500 trees and seven repetitions.
Microbial functional analysis
Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was used to infer putative functional metagenomes from 16S rRNA gene sequence profiles [42]. As the tool adapts OTUs with Greengene IDs, OTUs were picked with closed reference against the May 2013 Greengenes database. The relative abundance of each functional pathway was obtained for each sample, and non-microbial functional pathways belonging to the “Organismal Systems” and “Human Diseases” categories were excluded from downstream analysis. To determine metabolic features that were differentially abundant between each element status (low and high level), linear discriminant analysis effect size (LEfSe) was applied under the condition α = 0.05, with an LDA score of at least two [43].
Fecal elements analysis with inductively coupled plasma mass spectrometry (ICP-MS)
ICP-MS calibration standard solutions were prepared from 10 mg/L multi-element standard solution (PerkinElmer, Waltham, MA). The standard calibration solutions were prepared by dilution of the standard solutions with a suitable percentage of analytical grade concentrated HNO3 (Dongwoo Fine Chem.Co., Ltd., South Korea). De-ionized water (18.2 MΩ-cm) was prepared by a Milli-Q water purification system (Millipore, Bedford, MA). All chemicals and reagents used in this experiment were obtained from Sigma-Aldrich and of analytical reagent grade unless otherwise stated. Before use, all plastic and glassware were soaked in 10% nitric acid for at least 24 h and then rinsed with deionized water several times. To measure the total metal concentration in the samples, microwave-assisted acid digestion was performed. Fecal samples (~0.5 g) were weighed directly into quartz microwave digestion tubes and then 4.0 mL concentrated HNO3 was added. The 1.5-kW microwave was used to reach 230 °C within 20 min and the temperature was maintained for 15 min with a microwave digestion system (UltraWAVE; Milestone srl, Sorisole, Italy), followed by cooling without a microwave. The tube contents were then transferred to polypropylene tubes and diluted to 50.0 g with deionized water. Sample blanks were prepared by following the above procedure. ICP-MS measurements were performed using a quadrupole ELAN DRC-e spectrometer (Perkin-Elmer SCIEX, Norwalk, CT) equipped with a concentric nebulizer (Meinhard Associates, Golden, CO), a cyclonic spray chamber (Glass Expansion, Inc., West Melbourne, Australia), a quartz torch with a quartz injector tube (2 mm i.d.), and an autosampler (AS-93 Plus, Perkin-Elmer) for the simultaneous determination of metals. The following operational conditions were used, radiofrequency power of 1.4 kW and plasma, auxiliary, and nebulizer gas flow rates of 16, 1.2, and 0.9 L/min, respectively. Quality control was conducted throughout sample analysis. Quantitative analysis of the samples was performed by external calibration. To monitor the consistency of the instruments, the calibration standards were analyzed as samples regularly. Continuous calibration verification (CCV) standard solutions were measured after every ten samples. Data were accepted only when CCV samples were 90–110% of the expected value. Deionized water blanks were also analyzed at regular intervals to check for cross-contamination or losses.
NMR-based metabolomic analysis
For NMR-based metabolomic analysis, samples were prepared according to Lamichhane’s method with minor modifications [44]. Briefly, human fecal samples (~200 mg) were mixed with 1000 μL DDW, vortexed for 30 s and homogenized with a tissue homogenizer for 5 min. After centrifugation (14,000 × g, 4℃) for 10 min, 300 μL of supernatant was mixed with 60 μL deuterium oxide (D2O) containing 0.025 mg/mL 3-(trimethylsilyl) propionic acid-d4 sodium salt, 60 μL of 1 mM imidazole, 60 μL of 2 mM NaN3, and 120 μL of 0.5 M KH2PO4. The mixtures were vortexed for 1 min and centrifuged at 14,000 × g for 10 min. The clear supernatant was then transferred to a 5 mm NMR tube (Wilmad-LabGlass, Vineland, NJ) for NMR analysis. All 1H-NMR spectra were acquired using a Varian 500 MHz NMR system (Varian, Palo Alto, CA) equipped with a cold flow-probe. 1H-NMR spectra were collected at 25 °C using the water presaturation pulse sequence. Spectra were collected with 64 transients using a 4 s acquisition time and a 2 s recycle delay. Tentative assignments of 1H NMR signals were carried out using the Bayesian automated metabolite analyzer for NMR (BATMAN) and confirmed by Chenomx NMR Suite 8.3 (Chenomx Inc, Alberta, Canada) according to the Human Metabolome Database. A total of 67 metabolites (acetate, acetoin, alanine, arginine, aspartate, betaine, butyrate, cadaverine, carnitine, carnosine, cholate, choline, creatine, cysteine, folate, formate, fructose, fucose, fumarate, galactose, glucose, glutarate, glyceraldehyde, glycerol, glycine, heptanoate, histamine, histidine, hypoxanthine, indole, indoxyl sulfate, isobutyrate, isoleucine, isovalerate, lactate, leucine, lysine, malate, malonate, mannose, methionine, methyl succinate, N-acetylglutamate, N-acetylneuraminate, proline, propionate, purine, putrescine, pyrimidine, pyruvate, ribose, sarcosine, succinate, threonine, thymine, trimethylamine, trimethylamine oxide, tryptophan, tyrosine, uracil, urocanate, valerate, valine, xanthine, xylose, γ-aminobutyrate, and ρ-cresol) were assigned for analysis. The metabolomic data were imported into MetaboAnalyst 4.0 and normalized for multivariate pattern recognition analysis [45]. Partial least squares discriminant analysis (PLS-DA) was performed to obtain an overview of the complete metabolomic data set after mean centering scaling. Variable importance of projection (VIP) scores were assessed to rank the differential metabolites among each element status.
Biochemical measurements
Plasma triglyceride (DoGEN Bio Co., Ltd, Seoul, South Korea), glucose (Abcam, Cambridge, UK), insulin (Abcam), leptin (Abcam), and adiponectin (Abcam) concentrations were measured using commercial ELISA kits according to the manufacturer’s instructions. LPS levels were detected using an endpoint chromogenic endotoxin quantitative test (Signalway Antibody, College Park, MD).
SCFA measurement
Cecal SCFAs content was determined by gas chromatography. Cecal contents (~80 mg) were homogenized in 500 μL deionized water, after which the samples were acidified with 50 μL 50% sulfuric acid, followed by vortexing at room temperature for 5 min. After centrifugation at 14,000 × g for 10 min, 400 μL of the supernatant was transferred to a new tube, and 40 μL internal standard (1% 2-methyl pentanoic acid) and 400 μL anhydrous ethyl ether were added. The tube was vortexed for 1 min and then centrifuged at 14,000 × g for 10 min. The upper ether layer was used for further analysis. Volatile Free Acid Mix (Sigma-Aldrich) was used as the SCFA standard for the quantification of acetate, butyrate, isobutyrate, propionate, valerate, and isovalerate. GC-FID (GC 450; Bruker, Billerica, MA) was used to analyze SCFA content with fused silica capillary columns (Nukol, 30 m × 0.25 mm, 0.25-μm film thickness). The oven temperature was 170 °C, and the FID and injection ports were set to 225 °C. Nitrogen was used as the carrier gas and the sample injection volume was 2 μL.
RNA extraction and real-time PCR analysis
Total RNA was extracted from colon tissues (~50 mg) using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions, followed by concentration measurement. cDNA was synthesized from 1 μg of total RNA using Superscript IV Reverse Transcriptase (Thermo Fisher Scientific). Real-time PCR was performed using the LightCycler 480 detection system (Roche Diagnostics, Rotkreuz, Switzerland) and LightCycler 480 SYBR Green I Master; for primer sequences, see Appendix E. Samples were run in duplicate in a single 384-well reaction plate. Data were normalized to the housekeeping RPL19 gene and analyzed according to the ∆∆CT method.
Cell culture
The mouse macrophage cell line RAW264.7 was maintained in Dulbecco’s modified Eagle’s medium (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific) and 1% (v/v) penicillin (100 U/mL)-streptomycin (100 μg/mL) (Thermo Fisher Scientific). Cells were grown in 75 cm2 tissue culture flasks and incubated at 37 °C and 5% CO2. Cells were detached from the flask with a scraper and split 1:10 every five days. For nitrite measurement experiments, RAW264.7 cells (5 × 104 cells/well) were seeded in 24-well plates with 1 mL of medium per well. Cells were treated with LPS (100 ng/mL) as a positive control and a series of concentrations of Be (3, 30, and 100 ppb) after 24 h of inoculation. Fecal microbial supernatants and cell pellets from 1 mL of in vitro anaerobic culture with various concentrations of Be (3, 30, and 100 ppb) were also used to treat RAW264.7 cells.
Determination of NO production
The nitrite concentration in the culture medium was measured as an indicator of NO production using the Griess reaction. After incubation with test samples for 48 h, the supernatant from each well (50 μL) was transferred to a fresh 96-well plate, after which 25 μL of 1% sulfanilamide and 25 μL of 0.1% naphthyl-ethylenediamine in 5% HCl was added. After 10 min of incubation at room temperature, the absorbance of each well was measured at 540 nm using a Synergy HT microplate reader (Biotek, Winooski, VT). Relative nitrite production was calculated relative to the LPS only treatment group.
Statistical and subsequent bioinformatics analysis
Statistical analysis of all grouped data was performed using R software or GraphPad Prism 7 (GraphPad Software Inc., La Jolla, CA). Significance was determined using a two-tailed Student’s t test, Mann-Whitney test, or one-way ANOVA corrected for multiple comparisons with a Sidak test compared with the control group. Microbial data processing and multivariate statistical analysis were performed using MicrobiomeAnalyst. Permutational multivariate analysis of variance (PERMANOVA) was performed to test the association between microbiome composition and Be exposure based on NMDS. Association of fecal elements with MetS-related clinical biomarkers and gut microbiota was assessed by Spearman’s rank correlation analysis. A correlation heatmap was generated using the R package “Pheatmap.” P-values were adjusted for multiple testing with the Benjamini-Hochberg method. Multivariate analysis using a multivariate association with linear models (MaAsLin) was performed to identify significant associations of microbial abundances with metabolic status or fecal element status [46]. Age, sex, and smoking were treated as fixed effects, while MZ, DZ twin, and family relationships were treated as random variables. Low abundance taxa (the average relative abundance across all the samples < 0.1%) were excluded from MaAsLin analysis.