Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis
Abstract
:1. Introduction
2. Material and Methods
2.1. Preparation of Aged Mice
2.2. CD62p ELISA
2.3. Platelet Isolation
2.4. Western Blotting
2.5. Immunostaining
2.6. Proteomics
- Protein digestion: the samples were suspended in lysis buffer (8 M urea–0.1 M Tris-HCl buffer, pH 8.5) and protease inhibitor cocktail, followed by sonication for 20 min at 15 °C using Covaris S2 Focused Ultrasonicator (Covaris, Woburn, MA, USA). The Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, IL, USA) quantified the protein concentration. The digestion step was performed using filter-aided sample preparation on a Microcon 30 K centrifugal filter device (Millipore, Billerica, MA, USA). Each sample was reduced by incubating with Tris (2-carboxyethyl) phosphine (TCEP) at 37 °C for 30 min and alkylated with iodoacetic acid (IAA) at 25 °C for an hour in dark conditions. After washing with lysis buffer and 50 mM ammonium bicarbonate (ABC) sequentially, the proteins were digested with trypsin (enzyme to protein ratio of 1:50; w/w) at 37 °C for 18 h. The resulting peptide mixtures were transferred into new tubes, and trypsin was inactivated by acidifying with 15 μL of formic acid (Honeywell, Charlotte, NC, USA). The digested peptides were desalted using C18 spin columns (Harvard Apparatus, Holliston, MA, USA), and the peptides were eluted with 80% acetonitrile in 0.1% formic acid in water.
- LC–MS/MS analysis: The prepared samples were resuspended in 0.1% formic acid in water and analyzed using a Q-Exactive Orbitrap hybrid mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) along with an Ultimate 3000 system (Thermo Fisher Scientific, Waltham, MA, USA). We used a 2 cm × 75 μm ID trap column packed with 3 μm C18 resin and a 50 cm × 75 μm ID analytical column packed with 2 μm C18 resin for the peptides depending on the peptides’ hydrophobicity. The mobile phase solvents consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in 90% acetonitrile, whereas the flow rate was fixed at 300 nL/min. The gradient of the mobile phase was as follows: 4% solvent B for 14 min, 4%–15% solvent B for 61 min, 15%–28% solvent B for 50 min, 28%–40% solvent B for 20 min, 40%–96% solvent B for 2 min, holding at 96% of solvent B for 13 min, 96%–4% solvent B for 1 min, and 4% solvent B for 24 min. A data-dependent acquisition method was adopted, and the top 10 precursor peaks were selected and isolated for fragmentation. Ions were scanned in high resolution (70,000 in MS1, 17,500 in MS2 at m/z 400), and the MS scan range was 400–2000 m/z in both the MS1 and MS2 levels. Precursor ions were fragmented with a normalized collisional energy of 27%. Dynamic exclusion was set to 30 s.
- Proteome data analysis: Thermo MS/MS raw files of each analysis were searched using the Proteome Discoverer™ software (ver. 2.5), and the mouse database was downloaded from UniProt. The appropriate consensus workflow included a peptide-spectrum match validation step and a SEQUEST HT process for detection as a database search algorithm. The search parameters were set up as follows: 10 ppm of tolerances of precursor ion masses, 0.02 Da fragment ion mass, and a maximum of two missed cleavages with trypsin enzyme. The dynamic modification on the peptide sequence was as follows: static carbamidomethylation of cysteine (+57.012 Da), variable modifications of methionine oxidation (+15.995 Da), acetylation of protein N-term (+42.011 Da), and carbamylation of protein in N-term (+43.0006 Da). After searching, the data results below 1% of FDR were selected and filtered for at least six more peptide lengths. Precursor abundance calculation was based on intensity. Fold change was calculated in the protein abundance-based ratio. Furthermore, the p-values were calculated for the reported quantification ratios using a t-test based on the background.
2.7. Statistics
3. Results
3.1. Identification of Proteins with Increased or Decreased Expression in Aged Mice Platelets
3.2. Reconfirmation of Target Protein Expression in Aged Mice Platelets
3.3. Potential Targets of Antithrombotic Agent
- Beta-2-glycoprotein1 (β2GP1, Apolipoprotein H (ApoH)
- 2.
- Alpha-1-acid glycoprotein2 (AGP-1, Orm2)
- 3.
- Ras-related protein Rab-11A (Rab11a)
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Conflicts of Interest
References
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Lee, S.H.; Cho, S.; Lee, J.Y.; Hong, J.Y.; Kim, S.; Jeong, M.-H.; Kim, W.-H. Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis. Biomedicines 2023, 11, 2944. https://doi.org/10.3390/biomedicines11112944
Lee SH, Cho S, Lee JY, Hong JY, Kim S, Jeong M-H, Kim W-H. Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis. Biomedicines. 2023; 11(11):2944. https://doi.org/10.3390/biomedicines11112944
Chicago/Turabian StyleLee, Seung Hee, Suyeon Cho, Jong Youl Lee, Jung Yeon Hong, Suji Kim, Myong-Ho Jeong, and Won-Ho Kim. 2023. "Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis" Biomedicines 11, no. 11: 2944. https://doi.org/10.3390/biomedicines11112944
APA StyleLee, S. H., Cho, S., Lee, J. Y., Hong, J. Y., Kim, S., Jeong, M.-H., & Kim, W.-H. (2023). Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis. Biomedicines, 11(11), 2944. https://doi.org/10.3390/biomedicines11112944