MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/m_MTBLS5660_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/m_MTBLS5660_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/s_MTBLS5660.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/a_MTBLS5660_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/a_MTBLS5660_LC-MS_positive_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660/i_Investigation.txtprimary200OKftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS5660The acquired LC-MS raw data were analyzed by the progenesis QI software using the following parameters. Precusor tolerance was set 5 ppm, fragment tolerance was set 10 ppm, and retention time (RT) tolerance was set 0.02 min. Internal standard detection parameters were deselected for peak RT alignment, isotopic peaks were excluded for analysis, and noise elimination level was set at 10.00, minimum intensity was set to 15 % of base peak intensity. The Excel file was obtained with 3 dimension data sets including m/z, peak RT and peak intensities, and RT–m/z pairs were used as the identifier for each ion. The resulting matrix was further reduced by removing any peaks with missing value (ion intensity = 0) in more than 50 % samples. The internal standard was used for data QC (reproducibility). Metabolites were identified by progenesis QI Data Processing Software, based on public databases such as http://www.hmdb.ca/; http://www.lipidmaps.org/ and self-built databases. The positive and negative data were combined to get a combine data which was imported into R 'ropls' package. Principle component analysis (PCA) and (orthogonal) partial least-squares-discriminant analysis (O)PLS-DA were carried out to visualize the metabolic alterations among experimental groups, after mean centering (Ctr) and Pareto variance (Par) scaling, respectively. The Hotelling’s T2 region, shown as an ellipse in score plots of the models, defines the 95% confidence interval of the modelled variation. Variable importance in the projection (VIP) ranks the overall contribution of each variable to the OPLS-DA model, and those variables with VIP > 1 are considered relevant for group discrimination. In this study, the default 7-round cross-validation was applied with 1/seventh of the samples being excluded from the mathematical model in each round, in order to guard against overfitting. The differential metabolites were selected on the basis of the combination of a statistically significant threshold of variable influence on projection (VIP) values obtained from the OPLS- DA model and p values from a two-tailed Student’s t test on the normalized peak areas, where metabolites with VIP values larger than 1.0 and p values less than 0.05 were considered as differential metabolites.MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - reverse phaseAn ACQUITY UPLCHSS T3 column (100 mm × 2.1 mm, 1.8 um; Waters, Milford, MA, USA) was used for metabolite separation. Water (95%) and acetonitrile (5%) containing 0.1% formic acid were used as mobile phases A. Acetonitrile (47.5%) and isopropanol (47.5%) and Water (5%) containing 0.1% formic acid were used as mobile phases B. Linear gradient: 0.0-0.1 min at 5% B; 0.1-2 min from 5% B to 25% B; 2-9 min from 25% B to100% B and keep for 4 min; 13-13.1 min back to 0% B and 13.1-16 min at 0% B. The flow rate was 0.4 mL/min and column temperature was 40 ℃. All the samples were kept at 4 ℃ during the analysis. The injection volume was 2 μL.Integrated analysis of faecal microbial diversity and serum metabolome reveals the profiling of gut microbiota-related metabolites in rats and mice prolonged exposure to environmental high humidity.Guangdong Provincial Hospital of Chinese MedicineTaohua Lanserummass spectrometry assayPrior to analysis, samples were thawed at room temperature. 50 µL of serum sample from each subject was used for analysis. The serum was added to 200 µL of methanol and vortexed until the extract precipitated, and the protein was removed by centrifugation (4 °C, 10 min, 500×g). Then, the extract was lyophilized and stored at -80 °C. Before analysis, each sample was reconstituted with water/methanol (80:20, v/v). After vortexing and centrifugation, samples were transferred into a liquid chromatography–mass spectrometry (LC-MS) system for analysis. QC samples were prepared by mixing aliquots of the all samples to be a pooled sample.Rattus norvegicushttps://www.ebi.ac.uk/metabolights/MTBLS5660Taohua Lan. Department of Cardiology, Guangdong Provincial Hospital of Chinese Medicine. Department of Cardiology, Guangdong Provincial Hospital of Chinese Medicine, No. 111, Dade Road, Yuexiu Distric, Guangzhou, 510020, P. R. China.. 23959945@qq.com.No data transformation was performed in this study.Treatmentlantaohua123@163.comMale Sprague-Dawley rats (specific-pathogen-free, weighing 250 to 280 g) were housed under standard conditions with 12 hrs light/dark cycles and constant room temperature of 22 ± 2 ℃ and relative humidity of 60 ± 5%, and were fed with standard lab diet one week prior to further experiments. Rats were randomized into control group and dampness group. Rats in Dampness group were kept in room temperature of 22 ± 2 ℃ and relative humidity of 90 ± 5% for 7 days, 14 days and 28 days, respectively. Control rats were housed under standard conditions with room temperature of 22 ± 2 ℃ and relative humidity of 60 ± 5% for 7 days, 14 days and 28 days, respectively. Bloods were collected in 1.5 EP tube and centrifuged at 1000 × g for 15 min at 4 ℃ after 1 h at room temperature. Serum was separated and stored in -80 ℃ for assay.Metabolomicsmetaboliteuntargeted analysisVanquish Horizon systemRattus norvegicusexperimental blankIntegrated analysisserumgut microbiotaenvironmental high humidityQ Exactivemetaboliteuntargeted analysisVanquish Horizon systemRattus norvegicusexperimental blankIntegrated analysisserumenvironmental high humiditygut microbiotaQ ExactiveThe Q-Exactive mass spectrometer was equipped with heated electrospqray ionization(ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) and used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes.Data acquisition was performed in full scan mode ranging from 70 to 1050 (m/z) with a resolution of 70000 for MS1 and resolution17500 for MS2 was applied. Spray voltages (V) were set at 3500 for positive ionization mode and 2800 for negative ionization mode. Sheath gas and Aux gas flow rates were set at 40 and 10 arbitrary, respectively. Capillary and auxiliary gas heater temperatures were set at 320 ℃ and 400 ℃.Citric acidfalseIntegrated analysis of faecal microbial diversity and serum metabolome reveals the profiling of gut microbiota-related metabolites in rats and mice prolonged exposure to environmental high humidityBackground: High humidity, as a key climate risk factor, has become one of the significant threats to public health. However, less is known about the mechanism by which the high humidity environment affects the health of the population. The present study was designed to reveal the profile of gut microbiota-related metabolites in rats and mice prolonged exposure to high humidity environment. Methods: Sprague-Dawley rats and C57BL/6 mice were housed under standard conditions or prolonged exposure to high humidity environment for 7, 14 and 28 days, respectively. Integrated analysis of fecal microbial diversity and serum metabolome were performed by using 16S rRNA sequencing and nontargeted metabolomics with LC-MS/MS. Results: High humidity exposure led to significant changes in the composition of the gut microbiota and serum metabolic profiles in both rats and mouse models. Our results revealed that disorders in glycerophospholipid metabolism, ABC transporters, and Phenylalanine metabolism as key metabolic characteristics of hyperhumidity exposure. In addition, multi-omics correlation analysis identified the key gut microbiota-related metabolites, including Phosphocholine, Choline, LPC, Taurine, L-Valine, L-Proline, 2-Hydroxycinnamic acid, Phenylacetaldehyde, P-Salicylic acid and PC, which contributed to the pathogenic effect of high humidity. Conclusions: The present study revealed that high humidity exposure disrupts the host's metabolic homeostasis by altering the gut microbiota-related metabolites in rats and mouse models, showing commonalities and specificities.2026-06-162026-06-16MTBLS5660