Masshunter profinder software

Untargeted polar metabolites and nonpolar lipids were analyzed using an Agilent 6546 liquid chromatography time-of-flight mass spectrometer (LC-QToF) with an Agilent Jet Stream source coupled to an Agilent Infinity II UHPLC system (Agilent Technologies, Santa Clara, CA, USA), as previously published [31 (link)]. Collected metabolite data were processed using MassHunter Profinder software (Version 10.0, Agilent Technologies, Santa Clara, CA, USA), normalized to IS, and putatively identified against the Agilent METLIN Metabolite PCDL (G6825-90008, Agilent Technologies, Santa Clara, CA, USA) and a curated in-house PCDL based on retention time (±0.15 min), precursor ions, MSMS spectra and a library threshold score of more than 80%. For the acquired lipid data, auto MSMS data on polled PBQC samples were obtained at collisions of 20 eV and 35 eV. The acquired MSMS lipid data were analysed using the Agilent Lipid Annotator tool (V1.0; Santa Clara, CA, USA) which assigned isometric structures based on MSMS fragmentation patterns. Annotated lipids were then curated into a PCDL, which was used to identify lipids within the remaining analyzed samples with retention time thresholds (±0.15 min), MSMS spectra, and a library threshold score of more than 80%.

MassHunter Profinder software (Agilent, California, United States) was used to extract sample data for peak detection and alignment. Full scan mode was applied in the mass range of 80–1000 m/z. The initial and final retention times were set for data collection. The resultant data matrices were normalized using MetaboAnalyst 3.0¹ and then introduced to SIMCA-P 13.0 software (Umetrics, Umea, Sweden) for PCA and PLSDA analysis. PCA was used as an unsupervised pattern recognition approach to reduce the dimension of the UPLC-QTOF/MS data and disclose intrinsic clustering of samples. PLS-DA analysis was employed to maximize the differences in inter-class discrimination and minimize the differences in inter-class discrimination. The variables with VIP >1.5 and |p(corr)|≥0.58 in the PLS-DA analysis were further evaluated with an independent sample t-test.

Sample data was extracted by using MassHunter Profinder software (version B.06.00; Agilent, Santa Clara, CA, USA) for analysis and comparison. Data was normalized with MetaboAnalyst 4.0 and then SIMCA-P V14.1 (Umetrics, Umea, Sweden) was used for principal component analysis (PCA) and orthogonal-partial least squares-discriminant analysis (OPLS-DA). The potential biomarkers were identified based on precise molecular weight in the Human Metabolome Database (HMDB) and METLIN (http://metlin.scripps.edu/) database. MetaboAnalyst 4.0 (http://www.metaboanalyst.ca/) was visualized enrichment pathway of potential markers.

Data analysis was performed within Mass Hunter Profinder software (Agilent) for post-acquisition data processing. Then, the MetaboAnalyst database was used to normalize the original data. Multivariate data analysis was performed with SIMCA 14.0 (Umetrics, Sweden). OPLS-DA analysis based on non-targeted metabolomics was conducted in the serums between WT and WT + ANIT group, and WT and FXR-KO group (n: WT = 6, WT + ANIT = 6, FXR^−/− = 6). The endogenous metabolites with VIP >1.5 and |p(corr)| ≥ 0.58 were selected as the differential biomarker for further pathway enrichment analysis. MetaboAnalyst and DAVID database were used to analyze the metabolic pathway and KEGG pathway. Metabolite targets were collected through the MBrole database (http://csbg.cnb.csic.es/mbrole2/) and applied to the Sangerbox database (http://sangerbox.com/) for GO enrichment analysis and the Metascape database (https://metascape.org/gp/) for protein interaction analysis.

Furthermore, a MassHunter Profinder software (version B.06.00, Agilent, California, USA) was utilized to analyze the sample data for peak detection and alignment. Full scans mode was employed and the mass range was 80–1000 m/z. The biochemical online database HMDB database (https://www.hmdb.ca/) and METLIN (https://metlin.scripps.edu/) were used to identify the potential metabolites. MetaboAnalyst 4.0 was used for the pathway analysis. Finally, to identify and visualize the affected metabolic pathways, the biomarkers were put into MetaboAnalyst 4.0 based on the pathway library of Rattus norvegicus (rat). In the present study, the bioactive components, possible biomarkers and potential mechanisms of HG and [6]-GR in the treatment of CHF induced by DOX were comprehensively elucidated using the serum metabolomics strategy.