127 research outputs found

    A computational framework for the inference of protein complex remodeling from whole-proteome measurements

    No full text
    Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.ISSN:1548-7105ISSN:1548-709

    A computational framework for the inference of protein complex remodeling from whole-proteome measurements

    Full text link
    Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets

    Techno-Economic Analysis of Fluidized Bed Combustion of a Mixed Fuel from Sewage and Paper Mill Sludge

    Get PDF
    The treatment and disposal of sewage sludge is one of the most important and critical issues of wastewater treatment plants. One option for sludge liquidation is the production of fuel in the form of pellets from mixed sewage and paper mill sludge. This study presents the results of the combustion of pelletized fuels, namely sewage and paper mill sludge, and their 2:1 and 4:1 blends in a fluidized bed combustor. The flue gas was analysed after reaching a steady state at bed temperatures of 700ÔÇô800 ┬░C. Commonly used flue gas cleaning is still necessary, especially for SO2; therefore, it is worth mentioning that the addition of paper mill sludge reduced the mercury concentration in the flue gas to limits acceptable in most EU countries. The analysis of ash after combustion showed that magnesium, potassium, calcium, chromium, copper, zinc, arsenic, and lead remained mostly in the ash after combustion, while all cadmium from all fuels used was transferred into the flue gas together with a substantial part of chlorine and mercury. The pellets containing both sewage and paper mill sludge can be used as an environmentally friendly alternative fuel for fluidised bed combustion. The levelized cost of this alternative fuel is at the same current price level as lignite

    Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma

    Full text link
    BACKGROUND We have verified a mass spectrometry (MS)-based targeted proteomics signature for the detection of malignant pleural mesothelioma (MPM) from the blood. METHODS A seven-peptide biomarker MPM signature by targeted proteomics in serum was identified in a previous independent study. Here, we have verified the predictive accuracy of a reduced version of that signature, now composed of six-peptide biomarkers. We have applied liquid chromatography-selected reaction monitoring (LC-SRM), also known as multiple-reaction monitoring (MRM), for the investigation of 402 serum samples from 213 patients with MPM and 189 cancer-free asbestos-exposed donors from the United States, Australia, and Europe. RESULTS Each of the biomarkers composing the signature was independently informative, with no apparent functional or physical relation to each other. The multiplexing possibility offered by MS proteomics allowed their integration into a single signature with a higher discriminating capacity than that of the single biomarkers alone. The strategy allowed in this way to increase their potential utility for clinical decisions. The signature discriminated patients with MPM and asbestos-exposed donors with AUC of 0.738. For early-stage MPM, AUC was 0.765. This signature was also prognostic, and Kaplan-Meier analysis showed a significant difference between high- and low-risk groups with an HR of 1.659 (95% CI, 1.075-2.562; P = 0.021). CONCLUSIONS Targeted proteomics allowed the development of a multianalyte signature with diagnostic and prognostic potential for MPM from the blood. IMPACT The proteomic signature represents an additional diagnostic approach for informing clinical decisions for patients at risk for MPM

    A targeted mass spectrometry strategy for developing proteomic biomarkers : a case study of epithelial ovarian cancer

    Get PDF
    Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by Selected Reaction Monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion

    Benchmarking comes of age

    Get PDF
    • ÔÇŽ
    corecore