First, the normalized phosphoproteomic data were compared to the baseline (t = 0) to obtain the differential phosphorylation after 7 and 21 days. This analysis was performed by fitting a linear model for each phosphosite and contrast (7 days compared to baseline and 21 days compared to baseline) by using the R package limma (v3.34.9). Statistical significance was estimated using empirical Bayes method from the same package. The kinase-substrate background network was extracted from OmniPath database (as of 15 February 2019) (43). Using this previous knowledge along with the differential phosphorylation, the network analysis was performed using PHONEMeS (10) implemented with integer linear programming (ILP). This allowed us to extract the phosphorylation cascades from the data at both time points. To run the algorithm, we selected PRKAA1, PRKAA2, and MTOR as starting nodes (proteins), as indicated by the metabolomic data. The P value threshold was set to 0.1. The resulting network was visualized using Cytoscape (v3.7.1). The code used to perform the analysis can be found at https://github.com/saezlab/Hypertension_DAHL_rat.

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