DOI: 10.57760/sciencedb.o00019.00005
title: Effects of high fat diet during childhood on precocious puberty and gut microbiota in mice
title: Effects of high fat diet during childhood on precocious puberty and gut microbiota in mice

description: Untargeted Metabolomic Study Based on Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Cecum content (50 mg) were individually ground with liquid nitrogen and the homogenate was resuspended in pre-chilled 80% methanol (−20ºC) and then vortexed. The samples were incubated at −20°C for 60 min and then centrifuged at 14,000 g and 4°C for 20 min. The supernatants were subsequently transferred to a fresh tube and spun in a vacuum concentrator until dry. The dried metabolite pellets were reconstituted in 60 % methanol and analyzed by LC-MS/MS. LC-MS/MS analyses were performed using a Vanquish ultra-high-performance liquid chromatography (UHPLC) system (Thermo Fisher Scientific) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) operating in the data-dependent acquisition mode. Samples were injected into a Hyperil Gold column (100 × 2.1 mm, 1.9 μm; Thermo Fisher Scientific) using a 16-min linear gradient at a flow rate of 0.3 mL/min. The eluents for the positive polarity mode were eluent A (0.1 % aqueous formic acid solution) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mmol/L ammonium acetate, pH 9.0) and eluent B (methanol). Spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 35 arb, and auxiliary gas flow rate of 10 arb. The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (Thermo Fisher Scientific) to perform peak alignment, peak picking, and quantitation of each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal intensity tolerance, 30 %; signal/noise ratio, 3; and minimum intensity,100000. Afterward, peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. Then, the peaks were matched with the mzCloud (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/) databases to obtain accurate qualitative and relative quantitative results. The online KEGG database were used to identify metabolites by matching the molecular mass data. Finally, metabolites for separating the models were selected with the following requirements: variable importance in projection (VIP) ＞ 1 and |P(corr)| ≥ 0.5 with 95 % jack-knifed confidence intervals. The Student's t-test was applied to further analyze inter group significance of the selected metabolites. Pathway analysis and enrichment analysis of differential metabolites were conducted on the MetaboAnalyst web server (http://www.metaboanlyst.ca).
description: Untargeted Metabolomic Study Based on Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Cecum content (50 mg) were individually ground with liquid nitrogen and the homogenate was resuspended in pre-chilled 80% methanol (−20ºC) and then vortexed. The samples were incubated at −20°C for 60 min and then centrifuged at 14,000 g and 4°C for 20 min. The supernatants were subsequently transferred to a fresh tube and spun in a vacuum concentrator until dry. The dried metabolite pellets were reconstituted in 60 % methanol and analyzed by LC-MS/MS. LC-MS/MS analyses were performed using a Vanquish ultra-high-performance liquid chromatography (UHPLC) system (Thermo Fisher Scientific) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) operating in the data-dependent acquisition mode. Samples were injected into a Hyperil Gold column (100 × 2.1 mm, 1.9 μm; Thermo Fisher Scientific) using a 16-min linear gradient at a flow rate of 0.3 mL/min. The eluents for the positive polarity mode were eluent A (0.1 % aqueous formic acid solution) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mmol/L ammonium acetate, pH 9.0) and eluent B (methanol). Spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 35 arb, and auxiliary gas flow rate of 10 arb. The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (Thermo Fisher Scientific) to perform peak alignment, peak picking, and quantitation of each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal intensity tolerance, 30 %; signal/noise ratio, 3; and minimum intensity,100000. Afterward, peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. Then, the peaks were matched with the mzCloud (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/) databases to obtain accurate qualitative and relative quantitative results. The online KEGG database were used to identify metabolites by matching the molecular mass data. Finally, metabolites for separating the models were selected with the following requirements: variable importance in projection (VIP) ＞ 1 and |P(corr)| ≥ 0.5 with 95 % jack-knifed confidence intervals. The Student's t-test was applied to further analyze inter group significance of the selected metabolites. Pathway analysis and enrichment analysis of differential metabolites were conducted on the MetaboAnalyst web server (http://www.metaboanlyst.ca).