**2. Results**

All the horses included in the study were accustomed to the training program and showed no clinical sign of disease during the experimental period. The saliva collection method herein used was non-invasive and well tolerated by all horses.

1H-NMR spectroscopy allowed to quantify 46 metabolites in serum and 62 metabolites in saliva, including, among others, alcohols, amino acids, organic acids, carbohydrates and purine derivatives (Figures 1 and 2), with a spectrum of substances broadly in line with those found in the human metabolome [8,28].

Despite the detection of 30 shared metabolites, serum and salivary metabolomes resulted to be different, with 16 metabolites found only in serum, and 32 metabolites found only in saliva.

The concentrations of the metabolites that resulted to be statistically different between the samples collected before (T0) and after (T1) exercise in serum are reported in Table 1. *p*-values for all non-significant metabolites are reported in Supplementary Materials Table S1.

**Figure 1.** Portions of 1H-NMR spectra from typical serum samples. Assignments appear on the signals used for molecules quantification. The vertical scale of each portion is conveniently set to ease the signals observation.

To observe the overall variations intrinsic to the samples in the space constituted by this restricted group of metabolites, we calculated on their concentrations a robust Principal Component Analysis (rPCA) model (Figure 3). Three principal components (PCs) were accepted, the first of which accounted for 57.5% of the samples' variance represented by the model. Such a PC nicely accounted for the differences among the samples connected to exercise and showed that lactate, pyruvate, succinate, glycerol, fumarate and alanine mostly characterized the samples collected after exercise, while myo-inositol, histidine, proline, asparagine, glutamine and mannose mostly characterized the samples collected before exercise.

**Figure 2.** Portions of 1H-NMR spectra from typical saliva samples. Assignments appear on the signals used for molecules quantification. The vertical scale of each portion is conveniently set to ease the signals observation.

**Figure 3.** rPCA model built on the space constituted by the concentration of the molecules significantly different in serum, listed in Table 1. (**A**) In the score plot, samples collected at T0 and T1 are represented with black squares and red circles, respectively. The wide, empty circles represent the median of the groups. (**B**) The loading plot reports the correlation between the concentration of each substance and its importance over principal component (PC) 1. Significant correlations (*p* < 0.05) are highlighted with gray bars.


**Table 1.** Serum metabolites with significantly different concentration (μmol/L; mean ± SD) before (T0) and after (T1) exercise.

\* Increasing (↑) or decreasing (↓) trends from T0 to T1.

About the salivary metabolome, the concentrations of five molecules significantly increased after exercise, while nine significantly decreased between T0 and T1 (Table 2). *p*-values for all non-significant metabolites are reported in Supplementary Materials Table S2.

**Table 2.** Salivary metabolites with different concentrations (μmol/L; mean ± SD) before (T0) and after (T1) exercise.


\* Increasing (↑) or decreasing (↓) trends from T0 to T1.

To observe the overall trends associated with the samples, we calculated on their concentrations the rPCA model outlined in Figure 4. Three principal components (PCs) were accepted, the first of which accounted for 84.2% of the samples' variance represented by the model. Such PCs summarized the differences among the samples connected to exercise. The salivary metabolome of horses after exercise was mainly characterized by creatine, ornithine, phenylalanine and tyrosine, while horses before exercise were mainly characterized by fumarate, malate, malonate, 4-aminobutyrate, betaine and galactose. In Figure 5, the metabolic pathways overrepresented by the molecules significantly affected by exercise in serum and saliva are reported.

**Figure 4.** rPCA model built on the space constituted by the concentration of the molecules significantly different in saliva, listed in Table 2. (**A**) In the score plot, samples collected at T0 and T1 are represented with black squares and red circles, respectively. The wide, empty circles represent the median of the groups. (**B**) The loading plot reports the correlation between the concentration of each substance and its importance over PC 1. Significant correlations (*p* < 0.05) are highlighted with gray bars.

**Figure 5.** Biomolecular pathways overrepresentation analysis performed on the molecules listed in Table 1 for serum (red) and Table 2 for saliva (blue). The figure replicates a convenient portion of the biomolecular pathways overview according to Reactome, modified to show the main pathways and sub-pathways overrepresented as a consequence of exercise.
