Scaffold 4

The method for isolating EVs from serum was described above. We randomly picked up 3 subjects from the 11 subjects with elevated circulating EV numbers at T₀ compared to pre-HIIT-Ex. Briefly, circulating EVs were isolated from an equal amount (500 μl) of plasma between samples via qEV columns (Izon Science) according to the manufacturer’s instruction. Fractions 6–10 were collected from the columns, and EVs were precipitated using trichloroacetic acid, followed by reduction, alkylation with iodoacetamide, and trypsinization using an equal amount of protein between samples. Samples were separated using nano-LC-MS/MS, EASY-nLC 1200 (Thermo Fisher Scientific), and Q Exactive Plus (Thermo Fisher Scientific) at APRO Science Institute (Tokushima, Japan). Data were analyzed using Scaffold4 (Proteome Software, Portland, OR, United States) against the SwissProt database at APRO Science Institute. Quantitative value (normalized total spectra) on Scaffold4 (Proteome software Inc., Portland, OR, United States) was used for heatmap and protein interactions, which were generated using Heatmapper software (Babicki et al., 2016 (link)) and STRING v11.0 software (Szklarczyk et al., 2019 (link)), respectively.

Gel pieces were processed as described previously [28 (link)]. The bands were then reduced with 5 mM DTT, alkylated with 15 mM iodoacetamide, and digested with sequencing-grade trypsin (Promega) in 40 mM Tris pH 8.0. Peptides were extracted using 50% ACN and 5% formic acid followed by separation by reversed-phase chromatography on Acclaim PepMap100 C18 column (Thermo Scientific). The separated peptides were ionized with the Nanospray Flex Ion Source (Thermo Scientific) and introduced into a Fusion mass spectrometer (Thermo Scientific).
Data were analyzed using Proteome Discoverer 2.1 (Thermo Scientific). The Uniprot_Hum_Compl_20170714 database was used, and a reverse decoy protein database was run to eliminate false discovery. The results were introduced into Scaffold 4.8 (Proteome Software) for distribution. Lowest protein identification odds were set at ≥99.0% with 2 unique peptides at ≥99.0% minimum peptide identification probability.

For quantification of rod outer segment content of Gα_t and Gβ₁ subunits and comparative analysis of retinal morphology, data are presented as the mean ± SD. All statistical analyses were performed with GraphPad Prism 7.04. Morphologic data were analyzed using the nonparametric Mann–Whitney U test, and results were considered statistically significant at p < 0.05. For the MS identification of G-protein γ-subunits present in rod outer segments, Mascot data were imported into Scaffold 4.8 (Proteome Software) to merge all of the data for a sample represented by multiple gel bands, to estimate a confidence score for protein identification, and to perform a relative protein quantification based on the sum of intensities of the constituent peptides.