41 research outputs found

    CRAF R391W is a melanoma driver oncogene.

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    Approximately 75% of melanomas have known driver oncogenic mutations in BRAF, NRAS, GNA11 or GNAQ, while the mutations providing constitutive oncogenic signaling in the remaining melanomas are not known. We established a melanoma cell line from a tumor with none of the common driver mutations. This cell line demonstrated a signaling profile similar to BRAF-mutants, but lacked sensitivity to the BRAF inhibitor vemurafenib. RNA-seq mutation data implicated CRAF R391W as the alternative driver mutation of this melanoma. CRAF R391W was homozygous and over expressed. These melanoma cells were highly sensitive to CRAF, but not BRAF knockdown. In reconstitution experiments, CRAF R391W, but not CRAF WT, transformed NIH3T3 cells in soft-agar colony formation assays, increased kinase activity in vitro, induced MAP kinase signaling and conferred vemurafenib resistance. MAP kinase inducing activity was dependent on CRAF dimerization. Thus, CRAF is a bona fide alternative oncogene for BRAF/NRAS/GNAQ/GNA11 wild type melanomas

    Correlation of Aqueous, Vitreous, and Serum Protein Levels in Patients With Retinal Diseases.

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    PURPOSE To further establish aqueous humor (AH) as a clinically suitable source of protein biomarkers in retinal diseases by evaluating the correlation of a large panel of proteins between AH, vitreous humor (VH), and serum (SE). METHODS We enrolled 60 subjects (eyes) with various non-infectious retinal diseases. AH, VH, and SE proteins were analyzed using the Olink Target 96 platform (1196 protein assays in total). We compared these three matrices in terms of quantification overlap, principal component analysis, and correlation. RESULTS In the AH, VH, and SE samples, 841, 917, and 1133 proteins, respectively, were consistently quantified above the limit of detection in more than 30% of patients. AH and VH shared 812 of these proteins. AH and VH samples overlapped along principal component 1, but SE samples were distinct. We identified 490 proteins with significant (false discovery rate [FDR]-adjusted P 0.5) between AH and VH, compared to only 33 and 40 proteins for VH and SE and for AH and SE, respectively. CONCLUSIONS Due to a close correlation between protein concentrations in the AH and VH and a clear difference from the SE, AH has the potential to serve as a substitute for VH and may hold significance in identifying protein biomarkers and novel targets related to retinal diseases. TRANSLATIONAL RELEVANCE This study further supports AH as a clinically suitable source of protein biomarkers in retinal diseases. In addition, the identified AH and VH correlations can inform the selection of protein biomarker candidates in future translational research

    JUN dependency in distinct early and late BRAF inhibition adaptation states of melanoma.

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    A prominent mechanism of acquired resistance to BRAF inhibitors in BRAF (V600) -mutant melanoma is associated with the upregulation of receptor tyrosine kinases. Evidences suggested that this resistance mechanism is part of a more complex cellular adaptation process. Using an integrative strategy, we found this mechanism to invoke extensive transcriptomic, (phospho-) proteomic and phenotypic alterations that accompany a cellular transition to a de-differentiated, mesenchymal and invasive state. Even short-term BRAF-inhibitor exposure leads to an early adaptive, differentiation state change-characterized by a slow-cycling, persistent state. The early persistent state is distinct from the late proliferative, resistant state. However, both differentiation states share common signaling alterations including JUN upregulation. Motivated by the similarities, we found that co-targeting of BRAF and JUN is synergistic in killing fully resistant cells; and when used up-front, co-targeting substantially impairs the formation of the persistent subpopulation. We confirmed that JUN upregulation is a common response to BRAF inhibitor treatment in clinically treated patient tumors. Our findings demonstrate that events shared between early- and late-adaptation states provide candidate up-front co-treatment targets

    Alterations in Serum Polyunsaturated Fatty Acids and Eicosanoids in Patients with Mild to Moderate Chronic Obstructive Pulmonary Disease (COPD)

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    Smoking is a major risk factor for several diseases including chronic obstructive pulmonary disease (COPD). To better understand the systemic effects of cigarette smoke exposure and mild to moderate COPD—and to support future biomarker development—we profiled the serum lipidomes of healthy smokers, smokers with mild to moderate COPD (GOLD stages 1 and 2), former smokers, and never-smokers (n = 40 per group) (ClinicalTrials.gov registration: NCT01780298). Serum lipidome profiling was conducted with untargeted and targeted mass spectrometry-based lipidomics. Guided by weighted lipid co-expression network analysis, we identified three main trends comparing smokers, especially those with COPD, with non-smokers: a general increase in glycero(phospho)lipids, including triglycerols; changes in fatty acid desaturation (decrease in ω-3 polyunsaturated fatty acids, and an increase in monounsaturated fatty acids); and an imbalance in eicosanoids (increase in 11,12- and 14,15-DHETs (dihydroxyeicosatrienoic acids), and a decrease in 9- and 13-HODEs (hydroxyoctadecadienoic acids)). The lipidome profiles supported classification of study subjects as smokers or non-smokers, but were not sufficient to distinguish between smokers with and without COPD. Overall, our study yielded further insights into the complex interplay between smoke exposure, lung disease, and systemic alterations in serum lipid profiles

    Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification

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    Inflammatory bowel disease (IBD) represents a group of progressive disorders characterized by recurrent chronic inflammation of the gut. Ulcerative colitis and Crohn′s disease are the major manifestations of IBD. While our understanding of IBD has progressed in recent years, its etiology is far from being fully understood, resulting in suboptimal treatment options. Complementing other biological endpoints, bioanalytical “omics” methods that quantify many biomolecules simultaneously have great potential in the dissection of the complex pathogenesis of IBD. In this review, we focus on the rapidly evolving proteomics and lipidomics technologies and their broad applicability to IBD studies; these range from investigations of immune-regulatory mechanisms and biomarker discovery to studies dissecting host–microbiome interactions and the role of intestinal epithelial cells. Future studies can leverage recent advances, including improved analytical methodologies, additional relevant sample types, and integrative multi-omics analyses. Proteomics and lipidomics could effectively accelerate the development of novel targeted treatments and the discovery of complementary biomarkers, enabling continuous monitoring of the treatment response of individual patients; this may allow further refinement of treatment and, ultimately, facilitate a personalized medicine approach to IBD

    Phosphoproteomic Analysis of Platelets Activated by Pro-Thrombotic Oxidized Phospholipids and Thrombin

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    <div><p>Specific oxidized phospholipids (oxPC<sub>CD36</sub>) promote platelet hyper-reactivity and thrombosis in hyperlipidemia via the scavenger receptor CD36, however the signaling pathway(s) induced in platelets by oxPC<sub>CD36</sub> are not well defined. We have employed mass spectrometry-based tyrosine, serine, and threonine phosphoproteomics for the unbiased analysis of platelet signaling pathways induced by oxPC<sub>CD36</sub> as well as by the strong physiological agonist thrombin. oxPC<sub>CD36</sub> and thrombin induced differential phosphorylation of 115 proteins (162 phosphorylation sites) and 181 proteins (334 phosphorylation sites) respectively. Most of the phosphoproteome changes induced by either agonist have never been reported in platelets; thus they provide candidates in the study of platelet signaling. Bioinformatic analyses of protein phosphorylation dependent responses were used to categorize preferential motifs for (de)phosphorylation, predict pathways and kinase activity, and construct a phosphoproteome network regulating integrin activation. A putative signaling pathway involving Src-family kinases, SYK, and PLCγ2 was identified in platelets activated by oxPC<sub>CD36</sub>. Subsequent <i>ex vivo</i> studies in human platelets demonstrated that this pathway is downstream of the scavenger receptor CD36 and is critical for platelet activation by oxPC<sub>CD36</sub>. Our results provide multiple insights into the mechanism of platelet activation and specifically in platelet regulation by oxPC<sub>CD36</sub>.</p></div

    Phosphoproteome network regulating integrin in platelets activated by thrombin.

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    <p>Proteins and their sites of phosphorylation identified by mass spectrometry were mapped onto an integrin protein- interaction network (reference 32) as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084488#pone.0084488.s003" target="_blank">Materials and Methods S1</a>. Graphical representation was done with Cytoscape. Proteins are colored based on the changes in phosphorylation (yellow circle for at least one phosphorylation event induced by thrombin; white circle for no measurable change by thrombin). Phosphorylation sites are colored based on the up-regulation (red diamond), down-regulation (green diamond), or absence of modification (white diamond) by thrombin.</p

    KODA-PC induces platelet P-selectin expression in a SFK- and SYK-dependent manner.

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    <p><b>(A-B)</b> Human platelets isolated by gel filtration were incubated with 10 µM SFK inhibitor (PP2), 0.2 µM SYK inhibitor (BAY 61-3606), or controls (DMSO and 10 µM PP3) for 30 min. Then, 30 µM PLPC, 30 µM KODA-PC, 10 µM ADP, or 0.05 U/mL thrombin were added for 30 min. Platelet P-selectin expression was determined by flow cytometry using PE-conjugated antibody to P-selectin. <b>(A)</b> Flow cytometry histograms from representative experiments are shown. <b>(B)</b> Quantitation of flow cytometry data presented as mean ± SD of at least 3 independent experiments. * P<0.05.</p
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