MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12992/m_MTBLS12992_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12992/a_MTBLS12992_LC-MS_alternating_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12992/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12992/s_MTBLS12992.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12992Based on the log2 transformed raw data, differential metabolites are identified using both univariate and multivariate statistical analysis. Initially, the t.test function in R is employed for univariate analysis to test for significant differences in means between two groups of samples (P value < 0.05). Subsequently, multivariate analysis (Orthogonal Partial Least Square-Discriminant Analysis , OPLS-DA) are carried out using ropls R package with orthoI = NA set as default parameter. The Variable Importance in Projection (VIP) obtained from the OPLS-DA model (biological replicates ≥ 3) is used to preliminarily screen differential metabolites between different samples or groups (default threshold: VIP > 1). In addition, the P value/FDR (biological replicates ≥ 2) or fold change (FC) values from univariate analysis further aids in the selection of differential metabolites. Generally, variables meeting both criteria of P value < 0.05 and VIP > 1.0 are considered as differential metabolites.KEGG Enrichment AnalysisUtilizing the KEGG pathway database, Fisher’s exact test is employed to perform KEGG pathway enrichment analysis on the identified differential metabolites. The P value indicates whether differential metabolites are significantly enriched in certain functional pathways. A pathway is considered significantly enriched when P value < 0.05. The Fold Enrichment represents the ratio of GeneRatio to Background Ratio in the corresponding pathway, with higher values indicating greater enrichment.MSEA Enrichment AnalysisBased on all identified metabolites and the KEGG pathway database, Metabolite Set Enrichment Analysis (MSEA) is conducted using the R package corto (version 1.2.4), with the parameter np (Number of Permutations) set to 500. Similar to Gene Set Enrichment Analysis (GSEA), MSEA does not require specifying significantly upregulated or downregulated differential metabolites. Instead, it sets a series of metabolite sets, each representing a biological function, for enrichment analysis. Significant differential metabolite sets and associated pathways are identified through this analysis.Metabolic Network AnalysisBased on MSEA enrichment results, significant enriched pathways and differential metabolites within pathways are filtered using a P value threshold (usually <0.05), and a metabolic regulation network diagram is constructed using the R package ggraph (version 2.1.0). Colors and shapes are used to differentiate metabolites and pathways, as well as indicate the fold change (FC) of metabolites.MetaboLightsPublicLiquid Chromatography MS - alternating - reverse phaseSimultaneous Collection in Positive Ion Mode and Negative Ion Mode1. Chromatography column: Waters ACQUITY BEH C18 Column (1.7 µm × 2.1 mm × 100 mm).2. Mobile phase A: 0.1% formic acid in water, mobile phase B: 0.1% formic acid acetonitrile/methanol (40/60) solution;3. Column temperature: 40 °C; injection volume: 5 μL. Gradient elution conditions were as follows:Time (min)Flow (mL/min)A (%)B (%)0 min  0.25 mL/min  A 90%  B 10%  3 min  0.25 mL/min  A 60%  B 40%  5 min  0.25 mL/min  A 5%  B 95%  8 min  0.6 mL/min  A 0%  B 100%  10 min 0.6 mL/min  A 0%  B 100%  10.6 min 0.25 mL/min A 90%  B 10%  10.3 min 0.25 mL/min A 90%  B 10%   Collection in Negative Ion Mode only1. Chromatography column: Waters ACQUITY BEH C18 Column (1.7 µm × 2.1 mm × 100 mm).2. Mobile phase A: 6.5 mM ammonium bicarbonate in water; mobile phase B: 6.5 mM ammonium bicarbonate in methanol.3. Column temperature: 40 °C; injection volume: 5 μL. Gradient elution conditions were as follows:Time (min)Flow (mL/min)A (%)B (%)0 min  0.25 mL/min  A 90%  B 10%  4 min  0.25 mL/min  A 60%  B 40%  6 min  0.25 mL/min  A 5%   B 95%  8 min  0.4 mL/min   A 1%   B 99%  8.5 min 0.4 mL/min   A 1%   B 99%  8.6 min 0.25 mL/min  A 90%  B 10%  12 min  0.25 mL/min  A 90%  B 10%  Mycobacterium tuberculosis Infection Drives Osteoclast Overactivation via a2,3-Sialylation to Promote Pathological Bone Destruction.Zhiwei JiangSouthwest hospitalbone marrowmass spectrometry assay1. Cell collection: Centrifuge at 300xg, 4°C for 5 minutes, discard the supernatant medium, collect in a 2ml EP tube, then wash cells twice with 1xPBS. After each wash, obtain cell pellets by centrifugation (300xg, 4°C for 5 minutes). Place at -80°C for 15 minutes to fully deactivate enzymes. 2. Cell lysis and metabolite extraction: Add 200μL of 80% methanol solution (containing a mixture of internal standards), use an ultrasonic cell disruptor to lyse cells, then add 800μL of 80% methanol solution (containing a mixture of internal standards), vortex for 1 minute;3. Centrifuge at 14000xg, 4°C for 10 minutes, transfer the supernatant to a new 2ml centrifuge tube;4. PQC preparation: Mix an equal amount of supernatant from each sample in step (3) to create a PQC sample;5. After vacuum freeze-drying, resuspend in 100μL of 10% methanol/90% water solution, vortex for 30 seconds, sonicate for 1 minute, centrifuge at 14000xg, 4°C for 10 minutes, transfer the supernatant to a sample vial for mass spectrometry analysis. Mus musculusQuality control samples (PQC) are prepared by mixing sample extracts to analyze the reproducibility of samples under the same processing conditions. To ensure the stability of the detection process, internal standards with known concentrations are added to the samples. The smaller the response difference of the internal standards, the more stable the detection process and the higher the data quality.Utilizing the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, all detected metabolites are annotated, functionally defined, and classified. Visualization is performed using the ggplot2 (version 3.4.4) package.Based on the log2 transformed raw data, differential metabolites are identified using both univariate and multivariate statistical analysis. Initially, the t.test function in R is employed for univariate analysis to test for significant differences in means between two groups of samples (P value < 0.05). Subsequently, multivariate analysis (Orthogonal Partial Least Square-Discriminant Analysis , OPLS-DA) are carried out using ropls R package with orthoI = NA set as default parameter. The Variable Importance in Projection (VIP) obtained from the OPLS-DA model (biological replicates ≥ 3) is used to preliminarily screen differential metabolites between different samples or groups (default threshold: VIP > 1). In addition, the P value/FDR (biological replicates ≥ 2) or fold change (FC) values from univariate analysis further aids in the selection of differential metabolites. Generally, variables meeting both criteria of P value < 0.05 and VIP > 1.0 are considered as differential metabolites.Analysis             Software              Version  Visualization           ggplot2, pheatmap         3.4.4, 1.0.12  T test               stats (t.test)          4.3.2  Pearson Correlation Analysis    stats (cor)            4.3.2  PCA                 stats (prcomp)          4.3.2  OPLS-DA               ropls               1.34.0  MSEA                corto               1.2.4  Metabolite Network Analysis     ggraph              2.1.0  Fisher's exact test         stats (fisher.test)        4.3.2  BCGtreamenthttps://www.ebi.ac.uk/metabolights/MTBLS129921720239767@qq.comSeed normal BMMs onto a 6-well plate. After the cells adhere to the plate the next day, add RANKL to one group, and to another group, add BCG (MOI = 10) to infect the cells for 4 hours. After 4 hours, discard the original medium and replace it with DMEM high-glucose medium containing antibiotics and 10% serum. Change the medium every two days. Collect the cells when osteoclasts are observed under the microscope in the group treated with RANKL only.MetabolomicsInfectionpooled quality control sampleMus musculusBCG Vaccineuntargeted analysisThermo Scientific Vanquish Flex UHPLC SystemThermo Scientific Q Exactive HF-Xuntargeted metabolitesbone marrowexperimental sampleInfectionpooled quality control sampleMus musculusBCG Vaccineuntargeted analysisThermo Scientific Vanquish Flex UHPLC SystemThermo Scientific Q Exactive HF-Xuntargeted metabolitesbone marrowexperimental sampleMetabolites were separated using a Waters ACQUITY BEH C18 Column (1.7 µmx2.1 mm x100 mm) on a Vanquish Flex UPLC equipped with a refrigerated autosampler (10°C) and column heater (40°C).Two moblie phase condition was used to improve the metabolite coverage. For condition 1, solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile/methanol=4/6) were used to elute the metabolites with a 13.5 min gradient, as follows: 10% B at 0 min, 0.25ml/min; 40% B at 3 min, 0.25ml/min; 95 % B at 5 min, 0.25ml/min; 100 % B at 8 min, 0.6ml/min; 100 % B at 10 min, 0.6ml/min; and back to 10 % B at 10.5 min, 0.25ml/min; and equilibrate for 3min. Samples were analyzed using a Q Exactive HF-X (QE-HF-X) mass spectrometry equipped with a heated electro-spray ionization (HESI) source. All the data was acquired in positive and negtive switching mode using Full scan detection, and the PQC were also analyzed with Full scan/ddMS2 to acquire MS2 fragementation for metaoblite identificaiton and annotation. For conditon 2, solvent A was 6.5mM NH4HCO3 in water, solvent B was 6.5mM NH4HCO3 in methanol. The 12 min gradient was employed: 0 min, 0.25ml/min, 10% B; 4 min, 0.25ml/min, 40%B; 6 min, 0.25ml/min, 95%B; 8 min, 0.4ml/min, 99%B; 8.5 min, 0.4ml/min, 99%B; 8.6 min, 0.25ml/min, 10%B; 12 min, 0.25 ml/min, 10%B. Data acquisiton were performed only in negative mode, and Full scan was used for all the samples, and Full scan/ddMS2 was also used for PQC samples.The Full Scan settings were as follows: 60,000 resolution, AGC target, 1e6; Maximum IT, 100 ms; scan range, 60 to 900 m/z. For Full scan/ddMS2(DDA), Top 20 MS/MS spectral (dd-MS2) @ 15000 were generated with AGC target = 2e5, Maximum IT=25 ms, and (N)CE/stepped NCE = 10, 40, 80v. Metabolites detection and identification were performed using MS-dial (ver.5.1.230912) by searching against online database (MoNA, GNPS, HMDB and MS-dial database) and in-house database.nonefalseUntargeted LC-MS metabolomics of RANKL-differentiated Bone Marrow–Derived Macrophages ± Bacillus Calmette–Guérin (BCG) exposureTo investigate the metabolites and pathways modulated by BCG in RANKL-differentiated macrophages, thereby elucidating the metabolic mechanisms underlying BCG-induced macrophage activation, BMMs were stimulated with 50 ng/mL RANKL for 5 days to generate osteoclast-like cells. Subsequently, one group of cells was subjected to an additional 4-hour challenge with BCG (MOI = 10).2026-03-312025-09-13MTBLS12992HMDB0001294HMDB0094656