Buckets were then imported into SIMCA v13 software (Umetrics AB, Umea, Sweden) for multivariate statistical analysis. Buckets were mean-centered, scaled to unit variance (i.e., weighted by 1/SD for a given variable), and submitted to a principal components analysis (PCA) to ensure good homogeneity of data and possibly to exclude outliers. For this aim, data were visualized by score plots, where each point represents an NMR spectrum and thus a sample. Supervised analyses like orthogonal partial least square discriminatory analyses (OPLS-DA) were thus performed using the group belonging (control or exposed) as Y matrix. The number of components was determined using the cross-validation procedure that produces R2Y and Q2 factors (>0.5). Moreover, the reliability of our OPLS-DA model was assessed by a CV-ANOVA (cross-validated analysis of variance) test. The results were visualized by plotting the score of individuals relative to the first two components of the model. To highlight metabolites that are the most discriminating between controls and exposed, S-line was examined. This plot allows the visualization in a single graph, which mimics an NMR spectrum, the covariance (peak intensity), and correlation (peak color) between cKO and WT animals. Moreover, the signs of peaks in S-line give indications about the direction of change of metabolites—increased (positive peaks) or decreased (negative peaks) relative to WT.

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