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Smart metabolites database

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The Smart Metabolites Database is a comprehensive database that provides detailed information on a wide range of metabolites. The database includes chemical structures, physical properties, and analytical data for thousands of metabolites, making it a valuable tool for researchers and scientists working in the field of metabolomics.

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10 protocols using smart metabolites database

1

GC-MS/MS Metabolomic Profiling Protocol

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GC-MS/MS analysis was performed as previously described [49 (link)], using a GCMS-TQ8050 (Shimadzu Corporation, Kyoto, Japan). A 30 m × 0.25 mm (internal diameter) BPX-5 column (SGE, Melbourne, Australia) with a 0.25 µm film thickness was used, according to the method described in the Smart Metabolites Database (Shimadzu, Kyoto, Japan).
Data processing was performed using the Smart Metabolites Database (Shimadzu, Kyoto, Japan), MS-DIAL version 3.08 [50 (link)], and the MRMPROBS program version 2.42 [51 (link)]. Peaks were recorded for the 45−600 m/z mass range, and were automatically detected via MS-DIAL using the peak detection option of a minimum peak height of 2000. A data quality check was conducted using the thresholds of −10 < RI < 10, dot production > 0.8, and presence > 0.6, and the remaining data was then manually checked. Ultimately, 172 metabolites were identified in the plasma samples. The relative quantities of the metabolites were calculated using the peak areas of each metabolite relative to that of the internal standard (2-isopropylmalic acid), and expressed as a percentage of an arbitrary control set to 100%.
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2

Serum Metabolomics Analysis by GC/MS

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A serum metabolomics analysis was performed using GC/MS as described previously [24 (link)] with some modifications. In brief, a sample of 50
µl of serum was mixed with 5 µl of 1 mg/ml 2-isopropylmalic acid (Sigma-Aldrich, Tokyo, Japan) in distilled water as an internal
standard, and 250 µl of methanol–chloroform–water (2.5:1:1) mixture. Then samples were lyophilized, and added with 40 µl of 20 mg/mlmethoxyamine hydrochloride (Sigma-Aldrich), dissolved in pyridine for oximation. After mixing, the samples were shaken for 90 min at 30°C. Next 20 µl of N-methyl
N-trimethylsilyl-trifluoroacetamide (GL Science, Tokyo, Japan) was added for trimethylsilylation, and the mixture was incubated at 37°C for 45 min. The sample was subjected to GC/MS (GCMS
QP2010-Ultra; Shimadzu, Kyoto, Japan). The Shimadzu Smart Metabolites Database (Shimadzu) was used to identify metabolites. Samples were normalized by a pooled sample from control group. A
metabolic pathway analysis was performed using MetaboAnalyst [25 (link)]. Metabolites that significantly diffed between two groups were subjected to an
enrichment analysis (http://www.metaboanalyst.ca/faces/upload/EnrichUploadView.xhtml).
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3

Serum Metabolomics by GC/MS Analysis

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Serum metabolomics analysis was performed using GC/MS as described previously (Takemoto et al., 2017 (link)) with some modifications. A sample of 50 μL of serum was mixed with 5 μL of 1 mg/mL 2-isopropylmalic acid (Sigma-Aldrich, Tokyo, Japan) in distilled water as an internal standard, and 250 μL of a methanol–chloroform–water (2.5:1:1) mixture. Then, the samples were lyophilized, and 40 μL of 20 mg/mL methoxyamine hydrochloride (Sigma-Aldrich) was dissolved in pyridine for oximation. After mixing, the samples were shaken for 90 min at 30°C. Next, 20 μL of N-methyl N-trimethylsilyl-trifluoroacetamide (GL Science, Tokyo, Japan) was added for trimethylsilylation. The mixture was incubated at 37°C for 45 min. The sample was subjected to GC/MS (GCMS QP2010-Ultra; Shimadzu, Kyoto, Japan). The Shimadzu Smart Metabolites Database (Shimadzu) was used to identify metabolites. Samples were normalized by a pooled sample from the control group. A metabolic pathway analysis was performed using MetaboAnalyst (Xia and Wishart, 2011 (link)). Metabolites that significantly differed between the 2 groups were subjected to an enrichment analysis (http://www.metaboanalyst.ca/faces/upload/EnrichUploadView.xhtml).
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4

GC-MS Metabolite Profiling Protocol

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Analysis was performed by a GS-MS system (Shimadzu TQ-8050). Two μL of sample was injected with a split ratio of 1:10. Helium was used as a carrier gas at a constant flow rate of 1 mL/min. The GCMSsolution Smart MRM version 4.2. GC–MS analysis was utilized, and the running time of each sample was 67 min. The injection, interface, and ionization source temperatures were 280°C, 280°C, and 250°C, respectively. The initial temperature was set to 100°C, then raised to 320°C in a linear ramp of 4°C/min, and finally maintained at 320°C for 8 min. The mass fingerprints of 568 compounds are included in the Shimadzu Smart Metabolites Database: metabolites identified if their scanned ion pairs are 80% compatible with the database.
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5

Serum Metabolomics Analysis by GC/MS

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A serum metabolomics analysis was performed using GC/MS as described previously [34 (link)] with some modifications. In brief, a sample of 50 μL of serum was mixed with 5 μL of 1 mg/mL 2-isopropylmalic acid (Sigma-Aldrich, St. Louis, MO, USA) in distilled water as an internal standard, and 250 μL of methanol–chloroform–water (2.5:1:1) mixture. Then samples were lyophilized, and added with 40 μL of 20 mg/mL methoxyamine hydrochloride (Sigma-Aldrich), dissolved in pyridine for oximation. After mixing, the samples were shaken for 90 min at 30 °C. Next, 20 μL of N-methyl N-trimethylsilyl-trifluoroacetamide (GL Science, Tokyo, Japan) was added for trimethylsilylation, and the mixture was incubated at 37 °C for 45 min. The sample was subjected to GC/MS (GCMS QP2010-Ultra; Shimadzu, Kyoto, Japan). The Shimadzu Smart Metabolites Database (Shimadzu) was used to identify metabolites. Samples were normalized by a pooled all sample. All data are presented in Supplementary Tables S1 and S2 [35 (link)]. A metabolic pathway analysis was performed using MetaboAnalyst [36 (link)]. Metabolites that significantly differed between two groups were subjected to an enrichment analysis (http://www.metaboanalyst.ca/faces/upload/EnrichUploadView.xhtml, accessed on 1 June 2021).
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6

GC-MS Metabolite Identification Pipeline

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The derivatized milk sample (2 μl) or plasma sample (1 μl) was injected in spitless mode into GC-MS QP2020 NX (Shimadzu Corporation, Kyoto, Japan) and detailed parameter was previously described [26 (link)]. Metabolite identification was performed using the standard Smart Metabolites Database (Shimadzu Corporation), with each peak being automatically identified using free software MS-DIAL (version 4.80) and data analysis conducted using MetaboAnalyst (http://www.metaboanalyst.ca/).
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7

GC-MS Analysis of Metabolites

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These measurements were performed with reference to the previously reported methods [43 (link),44 (link),45 (link)]. GC-MS analysis was performed using a GC-MS QP2010 Ultra (Shimadzu, Kyoto, Japan) with a fused silica capillary column (BPX-5; 30 m × 0.25 mm inner diameter, film thickness: 0.25 μm; Shimadzu), a front inlet temperature of 250 °C, and a helium gas flow rate through the column of 39.0 cm/s. The column temperature was maintained at 60 °C for 2 min, then raised by increments of 15 °C/min to 330 °C, and maintained at that temperature for 3 min. The interface and ion source temperatures were 280 °C and 200 °C, respectively. All data obtained by GC-MS analysis were analyzed using MetaboAnalyst software (v. 5.0; Reifycs, Inc., Tokyo, Japan). The retention times indicated in the Smart Metabolites Database (Shimadzu) were used as references to create a library for data analysis. To perform a semi-quantitative assessment, the peak area of each quantified compound was calculated and normalized using the 2-isopropylmalate peak area.
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8

Urinary Metabolite Identification by GC-MS

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Gas chromatography-mass spectrometry solution (Shimadzu) was used to identify the urinary metabolites. Results were subsequently analyzed using a Microsoft Windows 7 Professional workstation and the Smart Metabolites Database (Shimadzu). Qualitative analysis was performed using the internal standard method, and the area ratio was determined as follows:
Area ratio = area value of compound Q.ION/area value of internal standard Q.ION, where Q.ION represents the peak area of a specific ion.
The relationships of detected metabolites were described on the metabolic pathway maps generated using the VANTED software (http://vanted.sourceforge.net/). Metabolite relationships were established using the KEGG pathway database (https://www.genome.jp/kegg/pathway.html).
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9

Quantitative GC-MS/MS Metabolite Analysis

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A GCMS-TQ8040 gas chromatograph–tandem mass spectrometer (Shimadzu, Kyoto, Japan) was used for analysis. A BPX-5 capillary column (30 m × 0.25 mm i.d., film thickness: 0.25 µm, SGE Analytical Science, Ringwood, Victoria, Australia) was used for the chromatographic separation. The column oven temperature was maintained at 60 °C for 2 min and then increased by 15 °C/min to 330 °C, with a final hold for 3 min. Interface and ion source temperatures were 280 °C and 200 °C, respectively. High-purity helium gas was used for carrier gas, and the flow rate was 1.14 mL/min. GC–MS/MS was conducted in the electron ionization positive mode. Ionization mode was at 70 eV. Selected reaction monitoring mode was used for analysis. Metabolites were identified by matching two transitions and their retention time with the Smart Metabolites Database (Shimadzu), performed using a built-in software GCMS solution (version 4.52, Shimadzu). The peak areas of each metabolite were normalized by 2-isopropyl-malic acid. Samples were automatically injected in the split mode and analyzed twice for various analyte intensities. The combination of split ratio and injection volume was 1:30 and 1 µL and 1:200 and 0.5 µL, respectively.
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10

GC/MS Liver Metabolomics Protocol

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A liver metabolomic analysis was performed using GC/MS as described previously (15) with some modifications. In brief, freeze-fractured liver samples (approximately 20 mg) were suspended in 250 μl of methanol-chloroform-water (2•5:1:1) and 5 μl of 1 mg/ml 2-isopropylmalic acid as an internal standard and homogenised using a polytron homogeniser (Microtec; China). Samples were subsequently mixed in a shaker at 1200 rpm at 37°C for 30 min and centrifuged at 16 000 g at 4°C for 5 min. Next, 160 μl of the supernatant was mixed with 200 μl of distilled water and vortexed, followed by centrifugation at 16 000 g at 4°C for 5 min; subsequently, 250 μl of the supernatant was dried under a vacuum using a centrifugal evaporator (RD-400; Yamato Scientific). Dried samples were pre-treated, derivatised and analysed by GC/MS (GCMS QP2010-Ultra; Shimadzu) within 24 h of derivatisation as described by Qiao et al. (15) . The Shimadzu Smart Metabolites Database (Shimadzu) was used to identify metabolites. The DNA content in the liver was analysed using the method by Labarca & Paigen (16) . The relative metabolite content was calculated as follows: the peak area of each metabolite was divided by that of the internal standard, 2-isopropylmalic acid, on the same chromatogram. It was further divided by the DNA content. The level for each metabolite in the control liver was set at 100.
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