TMT-based quantitative proteomics analysis reveals the effects of the α-lactalbumin peptides GINY and DQW on lipid deposition and oxidative stress in HepG2 cells
Description
To further explore the underlying mechanism of the α-lactalbumin peptides Gly-Ile-Asn-Tyr (GINY) and Asp-Gln-Trp (DQW) in alleviating hepatic steatosis, TMT-based quantitative proteomics was performed. Overall, a total of 3808 quantifiable proteins were identified from the four groups, and the quantitative results were visualized in Table S1. Among them, 213 proteins were identified as DEPs (Tables S2-4). Further analysis showed that 37 proteins were the mutual DEPs among the four groups (Table S5). As shown in Fig. 2b and Table S2, 155 proteins were identified as DEPs in the FFAs group (versus the control group). Compared with the FFAs group, 99 proteins were identified as DEPs in the GINY group (Table S3), and 89 proteins were identified as DEPs in the DQW group ( Table S4).
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Protein Digestion and Tandem Mass Tag (TMT)-labeling The experimental groups were divided into four groups: Control group (Con), FFAs group (FFAs), FFAs + GINY group (GINY) and FFAs + DQW group (DQW). There were three parallel samples in each group. In brief, total protein was extracted from HepG2 cells by SDT (4% SDS, 100 mM Tris-HCl, 1 mM DTT, 1% protease inhibitor, pH 8.0). After centrifugation, the protein concentration in the supernatant was determined using a BCA Protein Assay Kit. Two hundred micrograms of protein sample were incorporated into 30 μl SDT buffer (4% SDS, 100 mM DTT, 150 mM Tris-HCl, pH 8.0). The protein suspensions were then digested with 4 μg trypsin (Promega, USA) in 40 μl NH4HCO3 buffer overnight at 37 °C. The resulting peptides were desalted on C18 cartridges and the peptides were collected as a filtrate. For labeling, trypsin-digested peptide samples were labeled using TMT (Thermo Fisher Scientific, USA) reagent. LC-ESI Tandem MS (MS/MS) Analysis and Data Analysis Briefly, the LC-MS/MS analysis was performed on a Fusion orbitrap mass spectrometer that was coupled to Easy nLC (Thermo Fisher Scientific, USA). The peptide mixtures were injected onto a C18-reversed-phase column (Thermo Fisher Scientific, USA) and separated with a 90 min gradient at a flow rate of 250 nL/min. Buffer A and B were 0.1% formic acid and 80% acetonitrile with 0.1% formic acid, respectively. The mass spectrometer was operated in positive ion mode, and the peptides were recognized using a data-dependent method. Then, MS/MS spectra were recognized using the MASCOT engine embedded in Proteome Discoverer 1.4 against UniPort Homo sapiens database; the FASTA database contained 194609 protein sequences (UniPort_Homo_sapiens_194609_20210223, http://www.UniPort.org). For protein identification, the search options were as follows: peptide mass tolerance, 20 ppm; fragment mass tolerance, 0.1 Da; enzyme, trypsin; max missed cleavages, 2; fixed modification, carbamidomethyl (C), TMT 10plex (N-term), TMT 10plex (K); variable modification, oxidation (M), false discovery rate ≤ 0.01. Bioinformatic Analysis The fold change (≥ 1.2 or ≤ 0.83) and FDR (Benjamini-Hochberg method) adjusted p-value < 0.05 were used to identify differentially expressed proteins (DEPs) between two groups. The Gene Ontology (GO) terms of the differentially expressed proteins were annotated using the software Blast2GO (https://www.blast2go.com), including biological process (BP), cell component (CC) and molecular function (MF). For pathway analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEPs was performed using the DAVID database (https://david.ncifcrf.gov). Protein-protein interaction (PPI) were analyzed using STRING (http://string-db.org).