14 research outputs found

    Comprehensive Proteome Profiling of Platelet Identified a Protein Profile Predictive of Responses to An Antiplatelet Agent Sarpogrelate

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    Sarpogrelate is an antiplatelet agent widely used to treat arterial occlusive diseases. Evaluation of platelet aggregation is essential to monitor therapeutic effects of sarpogrelate. Currently, no molecular signatures are available to evaluate platelet aggregation. Here, we performed comprehensive proteome profiling of platelets collected from 18 subjects before and after sarpogrelate administration using LC-MS/MS analysis coupled with extensive fractionation. Of 5423 proteins detected, we identified 499 proteins affected by sarpogrelate and found that they strongly represented cellular processes related to platelet activation and aggregation, including cell activation, coagulation, and vesicle-mediated transports. Based on the network model of the proteins involved in these processes, we selected three proteins (cut-like homeobox 1; coagulation factor XIII, B polypeptide; and peptidylprolyl isomerase D) that reflect the platelet aggregation-related processes after confirming their alterations by sarpogrelate in independent samples using Western blotting. Our proteomic approach provided a protein profile predictive of therapeutic effects of sarpogrelate. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.1

    Integrated analysis of global proteome, phosphoproteome, and glycoproteome enables complementary interpretation of disease-related protein networks

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    Multi-dimensional proteomic analyses provide different layers of protein information, including protein abundance and post-translational modifications. Here, we report an integrated analysis of protein expression, phosphorylation, and N-glycosylation by serial enrichments of phosphorylation and N-glycosylation (SEPG) from the same tissue samples. On average, the SEPG identified 142,106 unmodified peptides of 8,625 protein groups, 18,846 phosphopeptides (15,647 phosphosites), and 4,019 N-glycopeptides (2,634 N-glycosites) in tumor and adjacent normal tissues from three gastric cancer patients. The combined analysis of these data showed that the integrated analysis additively improved the coverages of gastric cancer-related protein networks; phosphoproteome and N-glycoproteome captured predominantly low abundant signal proteins, and membranous or secreted proteins, respectively, while global proteome provided abundances for general population of the proteome. Therefore, our results demonstrate that the SEPG can serve as an effective approach for multi-dimensional proteome analyses, and the holistic profiles of protein expression and PTMs enabled improved interpretation of disease-related networks by providing complementary information.1

    Label-Free Quantitative Proteome Profiling of Cerebrospinal Fluid from a Rat Stroke Model with Stem Cell Therapy

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    Human adipose-derived mesenchymal stem cells (hAMSCs) are capable of immunomodulation and regeneration after neural injury. For these reasons, hAMSCs have been investigated as a promising stem cell candidate for stroke treatment. However, noninvasive experiments studying the effects of grafted stem cells in the host brain have not yet been reported. Cerebrospinal fluid (CSF), which can be collected without sacrificing the subject, is involved in physiological control of the brain and reflects the pathophysiology of various neurological disorders of the central nervous system (CNS). Following stem cell transplantation in a stroke model, quantitative analysis of CSF proteome changes can potentially reveal the therapeutic effect of stem cells on the host CNS. We examined hAMSC-secreted proteins obtained from serum-free culture medium by liquid chromatography-tandem mass spectrometry (LC-MS/MS), which identified several extracellular matrix proteins, supporting the well-known active paracrine function of hAMSCs. Subsequently, we performed label-free quantitative proteomic analysis on CSF samples from rat stroke models intravenously injected with hAMSC (experimental) or phosphate buffered saline (control). In total, 524 proteins were identified; among them, 125 and 91 proteins were increased and decreased with hAMSC treatment, respectively. Furthermore, gene set enrichment analysis revealed three proteins, 14-3-3 theta, MAG, and neurocan, that showed significant increases in the hAMSC-treated model; these proteins are core members of neurotrophin signaling, nerve growth factor (NGF) signaling, and glycosaminoglycan metabolism, respectively. Subsequent histological and neurologic function experiments validated proliferative neurogenesis in the hAMSC-treated stroke model. We conclude that (i) intravenous injection of hAMSCs can induce neurologic recovery in a rat stroke model and (ii) CSF may reflect the therapeutic effect of hAMSCs. Additionally, proteins as 14-3-3 theta, MAG, and neurocan could be considered as potential CSF biomarkers of neuroregeneration. These CSF proteome profiling results would be utilized as valuable resource in further stroke studies.1

    Estimating Influence of Cofragmentation on Peptide Quantification and Identification in iTRAQ Experiments by Simulating Multiplexed Spectra

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    Isobaric tag-based quantification such as iTRAQ and TMT is a promising approach to mass spectrometry-based quantification in proteomics as it provides wide proteome coverage with greatly increased experimental throughput. However, it is known to suffer from inaccurate quantification and identification of a target peptide due to cofragmentation of multiple peptides, which likely leads to under-estimation of differentially expressed peptides (DEPs). A simple method of filtering out cofragmented spectra with less than 100% precursor isolation purity (PIP) would decrease the coverage of iTRAQ/TMT experiments. In order to estimate the impact of cofragmentation on quantification and identification of iTRAQ-labeled peptide samples, we generated multiplexed spectra with varying degrees of PIP by mixing the two MS/MS spectra of 100% PIP obtained in global proteome profiling experiments on gastric tumor–normal tissue pair proteomes labeled by 4-plex iTRAQ. Despite cofragmentation, the simulation experiments showed that more than 99% of multiplexed spectra with PIP greater than 80% were correctly identified by three different database search enginesMODa, MS-GF+, and Proteome Discoverer. Using the multiplexed spectra that have been correctly identified, we estimated the effect of cofragmentation on peptide quantification. In 74% of the multiplexed spectra, however, the cancer-to-normal expression ratio was compressed, and a fair number of spectra showed the “ratio inflation” phenomenon. On the basis of the estimated distribution of distortions on quantification, we were able to calculate cutoff values for DEP detection from cofragmented spectra, which were corrected according to a specific PIP and probability of type I (or type II) error. When we applied these corrected cutoff values to real cofragmented spectra with PIP larger than or equal to 70%, we were able to identify reliable DEPs by removing about 25% of DEPs, which are highly likely to be false positives. Our experimental results provide useful insight into the effect of cofragmentation on isobaric tag-based quantification methods. The simulation procedure as well as the corrected cutoff calculation method could be adopted for quantifying the effect of cofragmentation and reducing false positives (or false negatives) in the DEP identification with general quantification experiments based on isobaric labeling techniques

    Prognostic plasma protein panel for Aβ deposition in the brain in Alzheimer's disease

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    Alzheimer's disease (AD) is the most common age-associated dementia. Many studies have sought to predict cerebral amyloid deposition, the major pathological hallmark of AD, using body fluids such as blood or cerebral spinal fluid (CSF). The use of blood in diagnostic procedures is widespread in medicine; however, existing blood biomarkers for AD remain unreliable. We sought to discover blood biomarkers that discriminate A beta deposition status in the brain. This study used 107 individuals who were cognitively normal (CN), 107 patients with mild cognitive impairment (MCI), and 40 AD patients with Pittsburg compound B positron emission tomography (PiB-PET) amyloid imaging data available. We found five plasma biomarker candidates via mass spectrometry (MS) based-proteomic analysis and validated these proteins using enzyme-linked immunosorbent assay (ELISA). Our integrated models were highly predictive of brain amyloid deposition, exhibiting 0.871 accuracy with 79% sensitivity and 84% specificity overall, and 0.836 accuracy with 68% sensitivity and 90% specificity in patients with MCI. These results indicated that a combination of proteomic-based blood proteins might be a possible biomarker set for predicting cerebral amyloid deposition.N

    Prognostic plasma protein panel for Aβ deposition in the brain in Alzheimer's disease

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    © 2019 Elsevier LtdAlzheimer's disease (AD) is the most common age-associated dementia. Many studies have sought to predict cerebral amyloid deposition, the major pathological hallmark of AD, using body fluids such as blood or cerebral spinal fluid (CSF). The use of blood in diagnostic procedures is widespread in medicine; however, existing blood biomarkers for AD remain unreliable. We sought to discover blood biomarkers that discriminate Aβ deposition status in the brain. This study used 107 individuals who were cognitively normal (CN), 107 patients with mild cognitive impairment (MCI), and 40 AD patients with Pittsburg compound B positron emission tomography (PiB-PET) amyloid imaging data available. We found five plasma biomarker candidates via mass spectrometry (MS) based-proteomic analysis and validated these proteins using enzyme-linked immunosorbent assay (ELISA). Our integrated models were highly predictive of brain amyloid deposition, exhibiting 0.871 accuracy with 79% sensitivity and 84% specificity overall, and 0.836 accuracy with 68% sensitivity and 90% specificity in patients with MCI. These results indicated that a combination of proteomic-based blood proteins might be a possible biomarker set for predicting cerebral amyloid deposition11Nsciescopu

    Blockers of Wnt3a, Wnt10a, or beta-Catenin Prevent Chemotherapy-Induced Neuropathic Pain In Vivo

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    Although chemotherapy is a key cancer treatment, many chemotherapeutic drugs produce chronic neuropathic pain, called chemotherapy-induced neuropathic pain (CINP), which is a dose-limiting adverse effect. To date, there is no medicine that prevents CINP in cancer patients and survivors. We determined whether blockers of the canonical Wnt signaling pathway prevent CINP. Neuropathic pain was induced by intraperitoneal injection of paclitaxel (PAC) on four alternate days in male Sprague-Dawley rats or male Axin2-LacZ knock-in mice. XAV-939, LGK-974, and iCRT14, Wnt/β-catenin blockers, were administered intraperitoneally as a single or multiple doses before or after injury. Mechanical allodynia, phosphoproteome profiling, Wnt ligands, and inflammatory mediators were measured by von Frey filament, phosphoproteomics, reverse transcription-polymerase chain reaction, and Western blot analysis. Localization of β-catenin was determined by immunohistochemical analysis in the dorsal root ganglia (DRGs) in rats and human. Our phosphoproteome profiling of CINP rats revealed significant phosphorylation changes in Wnt signaling components. Importantly, repeated systemic injections of XAV-939 or LGK-974 prevented the development of CINP in rats. In addition, XAV-939, LGK-974, and iCRT14 ameliorated CINP. PAC increased Wnt3a and Wnt10a, activated β-catenin in DRG, and increased monocyte chemoattractant protein-1 and interleukin-1β in DRG. PAC also upregulated rAxin2 in mice. Furthermore, β-catenin was expressed in neurons, including calcitonin gene–related protein-expressing neurons and satellite cells in rat and human DRG. In conclusion, chemotherapy increases Wnt3a, Wnt10a, and β-catenin in DRG and their pharmacological blockers prevent and ameliorate CINP, suggesting a target for the prevention and treatment of CINP. © 2020, The American Society for Experimental NeuroTherapeutics, Inc.1

    Fully Automated Multifunctional Ultrahigh Pressure Liquid Chromatography System for Advanced Proteome Analyses

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    A multifunctional liquid chromatography system that performs 1-dimensional, 2-dimensional (strong cation exchange/reverse phase liquid chromatography or SCX/RPLC) separations and online phosphopeptide enrichment using a single binary nanoflow pump has been developed. With a simple operation of a function selection valve equipped with a SCX column and a TiO<sub>2</sub> (titanium dioxide) column, a fully automated selection of three different experiment modes was achieved. Because the current system uses essentially the same solvent flow paths, the same trap column, and the same separation column for reverse-phase separation of 1D, 2D, and online phosphopeptides enrichment experiments, the elution time information obtained from these experiments is in excellent agreement, which facilitates correlating peptide information from different experiments. The final reverse-phase separation of the three experiments is completely decoupled from all of the function selection processes; thereby salts or acids from SCX or TiO<sub>2</sub> column do not affect the efficiency of the reverse-phase separation

    Proteogenomic Characterization of Human Early-Onset Gastric Cancer

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    We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs. © 2018 Elsevier Inc.Mun et al. perform proteogenomic analysis of diffuse gastric cancers (DGC) in a young population, identifying that correlations of mRNA-protein abundance associate with survival and defining four subtypes of DGC. The associations of some subtypes with related pathways are identified mainly by the proteomic data. © 2018 Elsevier Inc11Nsciescopu
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