Statistical analyses were performed after all data was checked for normality (Shapiro-Wilk test) and homogeneity of variances (Levene’s test). The analysis of variance was performed applying two-way ANOVA test, with diet (CTRL and GRA) and infection (Phdp) as independent factors. Significant differences were considered for p < 0.05. The statistic software package used was SigmaPlot 12 (Systat Software Inc., U.S.A.) and information regarding experimental unit and N is presented in Supplementary Fig. 1 (Fig. S1 ). Multivariate analysis (Partial Least Squares-Discriminant Analysis; PLS-DA) was also performed to find discriminative features among groups by means of the EZ-Info software (Umetrics, Sweden). The contribution of genes in the PCR-arrays to the PLS-DA models was assessed by means of variable importance in projection (VIP) measurements. A VIP score > 1 was considered an adequate threshold to determine discriminant variables in the PLS-DA model39 (link),40 (link).
Ezinfo software
Manufactured by Sartorius
Sourced in Sweden
EZinfo software is a data analysis tool developed by Sartorius. It provides multivariate data analysis and visualization capabilities, enabling users to interpret complex datasets effectively.
Automatically generated - may contain errors
Lab products found in correlation
Sigmaplot 12, by Grafiti LLC (1 mentions)
Progenesis qi software, by Waters Corporation (1 mentions)
Speedvac, by Thermo Fisher Scientific (1 mentions)
Progenesis qi, by Waters Corporation (1 mentions)
Acquity uplc beh c18 column, by Waters Corporation (1 mentions)
Markerlynx xs software, by Waters Corporation (1 mentions)
6 protocols using ezinfo software
1
Multivariate Analysis of Phdp Infection
2
Metabolomic Data Analysis Pipeline
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The metabolomics data were exported through MarkerLynx‐V4.1 software (Waters), including retention time (RT), value of m/z and area of the peak. The statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS‐DA) and orthogonal partial least squares discriminant analysis (OPLS‐DA), were performed using EZinfo software (Umetrics, Umeå, Sweden) and VIP value and independent t tests were examined by SPSS 19.0 software. The reference databases used to determine chemical structure were: HMDB (http://www.hmdb.ca ), Metlin (http://metlin.scripps.edu ) and PubChem (https://pubchem.ncbi.nlm.nih.gov ).
3
Respiratory Dynamics in Sea Bream and Sea Bass
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Daily variation of respiratory frequency and physical activity index in free-swimming sea bream and sea bass was analyzed by one-way ANOVA. Blood parameters of tagged and non-tagged (control) groups were compared by means of Student’s t-test. Analyses were performed using the SigmaPlot Version 13 for Windows (Systat Software Inc., Chicago, IL, United States). Multivariate partial least-squares discriminant analysis (PLS-DA) of data on respiratory frequency and physical activity index of exercised sea bream in the 1–6 BL/s water speed range was conducted with the EZ-info software (Umetrics, Sweden). The quality of the PLS-DA model was evaluated by R2Y and Q2Y parameters, which indicated the fitness and prediction ability, respectively.
4
Lipidomics Data Analysis Pipeline
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The original data collected from MS were imported with Progenesis QI software (version 2.0; Waters) to align, match and correct peaks. Then peaks were picked and lipids metabolites were identified by referring to the Human Metabolome Database (www.hmdb.ca ) and the Lipid Maps Database (www.lipidmaps.org ). The score of mass error, fragment and isotope similarity help identify the lipids in the Progenesis QI software. Then data sheets from the Progenesis QI software were obtained and absolute intensities of all identified compounds were recalculated to the relative abundances of the lipid molecules. After that, the data were exported to EZinfo software (version 2.0; Umetrics), and data analysis was performed to obtain group clusters such as unsupervised principal components analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The variable importance (VIP >1) was obtained from EZinfo software and P<0.05 (Student's t-test) of each identified metabolite was obtained from Progenesis QI to estimate the significance of the changes in the metabolites between the control and treated group. Volcano plot and pathway analysis were performed by MetaboAnalyst (http://www.metaboanalyst.ca ) which is an online metabolomic analysis software.
5
Mouse Liver Metabolomics by UPLC-MS
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For metabolomic analysis, mice liver was homogenized by Tissue Lyser. Briefly, 0.1 g liver tissues were abraded with 0.5 mL phosphate buffer saline (PBS), then vortexed with 600 μL 80% MeOH for 1 min, and ultrasonically extracted for 15 min and centrifuged at 14,000 rpm at 4 °C for 20 min.
Supernatant was transferred into a polypropylene tube, and the extraction process was repeated again as described above. The supernatants were evaporated to dryness by a vacuum rotation evaporator (SpeedVac, Thermo Fisher, Japan), then redissolved in 500 μL ACN/H2O (50:50, v/v, 0.1%FA) and filtered with 0.22 μm filter membrane.
Metabolomic data were performed on ThermoScientific UltraMate 3000 Dionex UPLC system coupled with Orbitrap ID-X Tribrid mass spectrometer (Fisher Scientific, Waltham, MA, USA) with ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm; Waters, USA) at 40℃. The mobile phase flow rate was 0.45 mL/min, with a gradient ranging from 2 to 100% for a total run of 15 min using water and acetonitrile, respectively, containing 0.1% formic acid as the mobile phases. Data processing and multivariate data analysis were conducted using Progenesis QI (Waters) by centroiding, integration, normalization, and deconvolution. The data matrix was further exported into EZInfo software (Umetrics) and transformed by an appropriate scaling technique for model construction.
Supernatant was transferred into a polypropylene tube, and the extraction process was repeated again as described above. The supernatants were evaporated to dryness by a vacuum rotation evaporator (SpeedVac, Thermo Fisher, Japan), then redissolved in 500 μL ACN/H2O (50:50, v/v, 0.1%FA) and filtered with 0.22 μm filter membrane.
Metabolomic data were performed on ThermoScientific UltraMate 3000 Dionex UPLC system coupled with Orbitrap ID-X Tribrid mass spectrometer (Fisher Scientific, Waltham, MA, USA) with ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm; Waters, USA) at 40℃. The mobile phase flow rate was 0.45 mL/min, with a gradient ranging from 2 to 100% for a total run of 15 min using water and acetonitrile, respectively, containing 0.1% formic acid as the mobile phases. Data processing and multivariate data analysis were conducted using Progenesis QI (Waters) by centroiding, integration, normalization, and deconvolution. The data matrix was further exported into EZInfo software (Umetrics) and transformed by an appropriate scaling technique for model construction.
6
LC/MS Data Analysis and Metabolite Identification
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The LC/MS raw data were processed by MarkerLynx XS software (Waters) using the following parameters: initial retention time 1 min, final retention time 8 min, mass tolerance 0.02 Da, intensity threshold 1000 counts, mass window 0.02 Da, retention time window 0.1 min, noise elimination level 6. The resulting multivariate data matrix was normalized with Pareto scaling and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA) using EZinfo software (Umetrics, Umea, Sweden). The preliminary molecular formula of marker candidates was calculated from the high resolution (HR) masses using carbon, hydrogen, nitrogen, and oxygen with mass tolerance <10 ppm and ring double bond (RDB) equivalent <50. The definitive elemental compositions were determined by isotope patterns and fragment ions in MS/MS spectra (Table 2). 3 and 1 H-NMR data including undescribed signals are consistent with literature's data. 9) 13 C-NMR spectral data, see Table 4.
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