A loading scatter plot was initially applied in OPLS-DA. As a result of filtering out the irrelevant orthogonal signal, it was more reliable to obtain metabolites with differences. The first principal component of Variable Importance in the Projection (VIP) was subsequently obtained to refine the analysis, and T test with Bonferroni correction was used to assess the difference via the SIMCA 14.0 software package (Umetrics AB, Umea, Sweden). A VIP that exceeded 1 and a p-value less than 0.05 indicated a changed biomarker [26 (link), 28 (link)]. The qualitative method of the metabolites matched the substance to a self-building standard substance database and the LECO-Fiehn Rtx5 database (Leco Corp., St. Joseph, MI, USA), which could execute automatic peak identification and the fidelity solution of the convolution, considering that metabolites with a similarity index (SI) greater than 70% represented reliable potential biomarkers [27 (link)].
Fiehn rtx5 database
Manufactured by Leco
Sourced in United States
The LECO-Fiehn Rtx5 database is a comprehensive library of mass spectra and retention time data for over 1,000 compounds. It is designed to assist in the identification of unknown compounds in complex samples using gas chromatography-mass spectrometry (GC-MS) analysis.
Automatically generated - may contain errors
7 protocols using fiehn rtx5 database
1
Metabolomics Biomarker Identification Protocol
2
GC-TOF-MS Metabolite Identification Protocol
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Chroma TOF 4.3X software and the LECO-Fiehn Rtx5 database (LECO Corporation, Benton Harbor, MI, USA) were used for raw peak extraction, database line filtering and calibration, peak alignment, deconvolution analysis, peak identification, and integration of peak area. Both mass spectrum match and retention index match were considered during metabolite identification. Additionally, peaks detected in less than 50% of the QC samples or for which relative standard deviation (RSD) greater than 30% in the QC samples were removed [50 (link),51 (link)]. Metabolites separated by GC-TOF-MS were identified using LECO ChromaTOF 4.3X software and the LECO/Fiehn Rtx5 metabolite mass spectral library by matching the mass spectrum and retention index.
3
Metabolomics Profiling Using Chroma TOF4.3X
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Chroma TOF4.3X software (LECO Corporation) and LECO-Fiehn Rtx5 database (LECO Corporation) were used for data baseline filtering, raw peak exaction, peak alignment and identification, calibration of the baseline, deconvolution analysis and integration of the peak area. The peaks were normalized to the sum of spectrum prior to multivariate analyses. All raw data were analyzed by PCA, PLS-DA and OPLS-DA using SIMCA-P+11.5 software (MKS Data Analytics Solutions, Malmö, Sweden) after performing a unit variance procedure. To further identify potential biomarkers, commercial databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp/kegg/ ) and PubChem Compound (https://pubchem.ncbi.nlm.nih.gov/ ), were used to search for metabolites. Then physiological, biochemical and pathological information was collected from CytoKEGG (Cytoscape 3.0.1 software; http://www.cytoscape.org/ ) to analyze the potential biomarkers involved in the metabolic pathways (19 (link)). Data were processed by SPSS 17.0 (SPSS, Inc., Chicago, IL, USA) and statistical analyses were performed using the one-way analysis of variance (ANOVA) among multiple groups followed by Fisher's least significant difference test. P<0.05 was considered to indicate a statistically significant difference.
4
Metabolite Identification and Pathway Analysis
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Chroma TOF v. 4.3X software (https://www.lecosoftware.com/chromatof ) and the LECO-Fiehn Rtx5 database (LECO Corporation, St. Joseph, MI, USA) were used to identify raw peaks, filter and calibrate data baselines, align and identify the peaks, integrate their areas, and perform a deconvolution analysis101 (link). Mass spectrum and retention index matching were considered in metabolite identification. Peaks detected in < 50% and RSD in > 30% of the QC samples were removed102 (link). Differential metabolite screening was conducted using previously published methods93 and peaks with similarity greater than 700, variable importance projection (VIP) exceeding 1.0 and P < 0.05 by t-test were selected as the reliable differentially expressed metabolites. KEGG database was used to search for differential metabolite pathways103 (link),104 (link).
5
Metabolomic Analysis Workflow for Biomarker Discovery
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Chroma TOF 4.3X software and LECO-Fiehn Rtx5 database (LECO Corp, St. Joseph, MI, United States) were used to extract raw peaks, filter and calibrate data baselines, align peak, analyze deconvolution, identify peak and integrate the peak area (Kind et al., 2009 (link)). Peaks were identified by retention time index (RI), with a RI tolerance of 5000. The obtained three-dimensional data including the peak number, sample name, and normalized peak area were processed by SIMCA14.1 software package (V14.1, MKS Data Analytics Solutions, Umea, Sweden) for principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA). In OPLS-DA model, the first principal component of variable importance in the projection (VIP) values exceeding 1.0 are most relevant and were first selected as changed metabolites. The remaining variables were then assessed by Student’s t-test (P-value <0.05). In addition, databases including KEGG1 and NIST2 were applied to identify the metabolites involved pathways. MetaboAnalyst3 also used for pathway analysis.
6
GC-TOF MS Metabolite Extraction Protocol
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The GC quadrupole TOF MS analysis and metabolites extraction were similar to a previous study (Zhang et al., 2019b) . Briefly, 450 μL of methanol/chloroform (volumetric ratio = 3:1) was added to 50 mg samples from PREP and POSP to extract metabolites. Equal aliquots of extract liquid from all experimental samples were pooled as quality control (QC) specimens. Adonitol was utilized as an internal standard. To perform the following GC TOF MS analysis of all samples, an Agilent 7890 GC system was used along with a Pegasus HT TOF mass spectrometer in splitless mode (LECO Corporation). For 1 min, the initial temperatures were maintained at 50°C, then incremented to 310°C at a rate of 10°C min -1 and kept at 310°C for 8 min. The ion source, injection, and transfer line temperatures were 250, 280, and 280°C, respectively. The MS data were obtained in a full-scan mode after a solvent delay of 6.33 min with the m/z range of 50-500 at a rate of 12.5 spectra per second. The Chroma TOF 4.3X software built-in with the LECO-Fiehn Rtx5 database (LECO Corporation) was used to preprocess and annotate the metabolomics data. The peaks detected less than 50% of QC specimens or relative standard metabolomics data deviation of more than 30% in QC specimens were eliminated (Dunn et al., 2011) .
7
Metabolomics Data Analysis Protocol
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A Chrome TOF4.3X software and a LECO-Fiehn Rtx5 database (LECO Corporation, St. Joseph, MI, USA) were used for raw peaks exacting, baselines filtering and calibration, peak alignment, deconvolution analysis, peak identification and integration of the peak area (Kind et al., 2009) (link). The retention time index (RI) method was used in peak identification with 5000 of the RI tolerance. The data quality control was firstly performed by removing metabolites with missing values in more than 30% of all the individuals. Then, the data were filtered by using the interquartile range method, filling up the missing values of raw data by half of the minimum value and standardizing by the internal standard normalization method.
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