• Title/Summary/Keyword: Shotgun proteomics

Search Result 13, Processing Time 0.02 seconds

Comparative secretome analysis of human follicular dermal papilla cells and fibroblasts using shotgun proteomics

  • Won, Chong-Hyun;Kwon, Oh-Sang;Kang, Yong-Jung;Yoo, Hyeon-Gyeong;Lee, Dong-Hun;Chung, Jin-Ho;Kim, Kyu-Han;Park, Won-Seok;Park, Nok-Hyun;Cho, Kun;Kwon, Sang-Oh;Choi, Jong-Soon;Eun, Hee-Chul
    • BMB Reports
    • /
    • v.45 no.4
    • /
    • pp.253-258
    • /
    • 2012
  • The dermal papilla cells (DPCs) of hair follicles are known to secrete paracrine factors for follicular cells. Shotgun proteomic analysis was performed to compare the expression profiles of the secretomes of human DPCs and dermal fibroblasts (DFs). In this study, the proteins secreted by DPCs and matched DFs were analyzed by 1DE/LTQ FTICR MS/MS, semi-quantitatively determined using emPAI mole percent values and then characterized using protein interaction network analysis. Among the 1,271 and 1,188 proteins identified in DFs and DPCs, respectively, 1,529 were further analyzed using the Ingenuity Pathway Analysis tool. We identified 28 DPC-specific extracellular matrix proteins including transporters (ECM1, A2M), enzymes (LOX, PON2), and peptidases (C3, C1R). The biochemically-validated DPC-specific proteins included thrombospondin 1 (THBS1), an insulin-like growth factor binding protein3 (IGFBP3), and, of particular interest, an integrin beta1 subunit (ITGB1) as a key network core protein. Using the shotgun proteomic technique and network analysis, we selected ITGB1, IGFBP3, and THBS1 as being possible hair-growth modulating protein biomarkers.

Proteomic analysis of Korean mothers' human milk at different lactation stages; postpartum 1, 3, and 6 weeks (출산 후 경과한 날에 따른 한국인 산모의 모유 단백체 분석)

  • Park, Jong-Moon;lee, Hookeun;Song, Seunghyun;Hahn, Won-Ho;Kim, Mijeong;Lee, Joohyun;Kang, Nam Mi
    • Analytical Science and Technology
    • /
    • v.30 no.6
    • /
    • pp.348-354
    • /
    • 2017
  • In this study, patterns of proteome expression were monitored and specifically expressed proteins in human milk were detected in collected human milk after 1 week, 3 weeks, and 6 weeks from delivery. A quantitative shotgun proteomic approach was used to identify human milk proteins and reveal their relative expression amounts. For each sample, two independent human milk samples from two mothers were pooled, and then three replicated shotgun proteomic analyses were carried out. Casein, which is a highly abundant protein in human milk, was removed, and then trypsin was treated to produce a digested peptide mixture. The peptides were loaded in the home-made reversed-phase C18 fused-silica capillary column, and then the eluted peptides were analyzed by using a linear ion-trap mass spectrometer. The relative quantitation of proteins was performed by the normalized spectral count method. For each sample, 81-109 non-redundant proteins were identified. The identified proteins consisted of glycoproteins, metabolic enzyme, and chaperon enzymes such as lactoferrin, carboxylic ester hydrolase, and clusterin. The comparative analysis for the 63 proteins, which were reproducibly identified in all three replications, revealed that 25 proteins were statically significant differentially expressed. Among the differentially expressed proteins, Ig lambda-7 chain C region and tenascin drastically decreased with the delivery time.

Reduction of Ambiguity in Phosphorylation-site Localization in Large-scale Phosphopeptide Profiling by Data Filter using Unique Mass Class Information

  • Madar, Inamul Hasan;Back, Seunghoon;Mun, Dong-Gi;Kim, Hokeun;Jung, Jae Hun;Kim, Kwang Pyo;Lee, Sang-Won
    • Bulletin of the Korean Chemical Society
    • /
    • v.35 no.3
    • /
    • pp.845-850
    • /
    • 2014
  • The rapid development of shotgun proteomics is paving the way for extensive proteome profiling, while providing extensive information on various post translational modifications (PTMs) that occur to a proteome of interest. For example, the current phosphoproteomic methods can yield more than 10,000 phosphopeptides identified from a proteome sample. Despite these developments, it remains a challenging issue to pinpoint the true phosphorylation sites, especially when multiple sites are possible for phosphorylation in the peptides. We developed the Phospho-UMC filter, which is a simple method of localizing the site of phosphorylation using unique mass classes (UMCs) information to differentiate phosphopeptides with different phosphorylation sites and increase the confidence in phosphorylation site localization. The method was applied to large scale phosphopeptide profiling data and was demonstrated to be effective in the reducing ambiguity associated with the tandem mass spectrometric data analysis of phosphopeptides.