The hippocampi from all four groups in this study were processed simultaneously for proteomics using the same protocol and the instrument that was used in our previous study (33). We used the vehicle and KA (with no surgery) group's raw data from our recently published study (33) to compare with surgery groups. All four raw data sets were reanalyzed with Proteome Discoverer 2.2.0.388. The data were searched using Mascot 2.2.07 against Uniprot-Mus musculus with quantification using the Minora feature detector. Peptide validation was performed using the Percolator node within Proteome Discoverer. The searches were performed with static modifications of carbamidomethyl (Cys), dynamic modifications of oxidation (Met), and deamidation (Asn, Gun).

Further analysis was performed using the R package MethaboanalystR 2.0 (34), which contains R functions and libraries in the MetaboAnalyst webserver (35). Upon checking the data integrity as satisfactory (i.e., no peptide with more than 50% missing replicates, and positive values for the area), missing value estimation was imputed using the Singular Value Decomposition (SVD) method. Non-informative values that were near-constant throughout the experimental conditions were detected using the interquartile range (IQR) estimation method and deleted. Data were normalized using the Quantile normalization method. Data transformation was performed based on Generalized Logarithm Transformation (glog) to make individual features more comparable. The group samples were compared by t-test for paired groups with the adjusted p-value and False Discovery Rate (FDR) set at 0.01. Fold change analysis with a threshold of 2 was performed to compare the absolute value of change between group values (for paired groups). A volcano plot was created to combine the fold change and the two-sample t-test analysis. The PCA analysis was performed using the prompt package, and pairwise score plots were created to provide an overview of the various separation patterns among the most significant components. Partial least squared (PLS) regression was then performed using the plsr function provided by the R pls package to predict the continuous and discrete variables. A PLS-DA model was built to classify and cross-validated PLS using the caret package. The uniport protein ids that were altered with the p < 0.01 were used to retrieve the corresponding KEGG ids using the “Retrieve/ID mapping” tool of UniProt (accessible at http://www.uniprot.org/uploadlists/). KEGG ids were then used to retrieve the biological pathway association of the proteins. Enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) 6.8 Tools (36, 37). The one-way analysis of variance (ANOVA) was used to determine whether there were any significant differences between the means ±SEM within four groups. Post-hoc analysis was performed with Fisher's Least Significant Difference method. Proteins with FDR values <0.01 were considered significant. Box plots were created for all significantly altered proteins determined by ANOVA (Supplementary Figure 1).

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