Relevant bibliographies by topics / Proteome proteomics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Proteome proteomics'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Proteome proteomics"

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Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta, and Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.

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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the “wiring diagram” of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.
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Thanasupawat, Thatchawan, Aleksandra Glogowska, Christopher Pascoe, Sai Nivedita Krishnan, Maliha Munir, Farhana Begum, Jason Beiko, et al. "Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes." International Journal of Molecular Sciences 22, no. 17 (September 3, 2021): 9566. http://dx.doi.org/10.3390/ijms22179566.

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Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan® signatures and functionalities in patient GBM cells.
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Sadeesh, Nithin, Mauro Scaravilli, and Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes." Cancers 13, no. 19 (September 27, 2021): 4829. http://dx.doi.org/10.3390/cancers13194829.

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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.
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Burat, Bastien, Audrey Reynaerts, Dominique Baiwir, Maximilien Fléron, Gauthier Eppe, Teresinha Leal, and Gabriel Mazzucchelli. "Characterization of the Human Eccrine Sweat Proteome—A Focus on the Biological Variability of Individual Sweat Protein Profiles." International Journal of Molecular Sciences 22, no. 19 (October 8, 2021): 10871. http://dx.doi.org/10.3390/ijms221910871.

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The potential of eccrine sweat as a bio-fluid of interest for diagnosis and personalized therapy has not yet been fully evaluated, due to the lack of in-depth sweat characterization studies. Thanks to recent developments in omics, together with the availability of accredited sweat collection methods, the analysis of human sweat may now be envisioned as a standardized, non-invasive test for individualized monitoring and personalized medicine. Here, we characterized individual sweat samples, collected from 28 healthy adult volunteers under the most standardized sampling methodology, by applying optimized shotgun proteomics. The thorough characterization of the sweat proteome allowed the identification of 983 unique proteins from which 344 were identified across all samples. Annotation-wise, the study of the sweat proteome unveiled the over-representation of newly addressed actin dynamics, oxidative stress and proteasome-related functions, in addition to well-described proteolysis and anti-microbial immunity. The sweat proteome composition correlated with the inter-individual variability of sweat secretion parameters. In addition, both gender-exclusive proteins and gender-specific protein abundances were highlighted, despite the high similarity between human female and male sweat proteomes. In conclusion, standardized sample collection coupled with optimized shotgun proteomics significantly improved the depth of sweat proteome coverage, far beyond previous similar studies. The identified proteins were involved in many diverse biological processes and molecular functions, indicating the potential of this bio-fluid as a valuable biological matrix for further studies. Addressing sweat variability, our results prove the proteomic profiling of sweat to be a promising bio-fluid analysis for individualized, non-invasive monitoring and personalized medicine.
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Senavirathna, Lakmini, Cheng Ma, Ru Chen, and Sheng Pan. "Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity." Cells 11, no. 15 (August 7, 2022): 2450. http://dx.doi.org/10.3390/cells11152450.

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Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.
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Solovyeva, Elizaveta M., Julia A. Bubis, Irina A. Tarasova, Anna A. Lobas, Mark V. Ivanov, Alexey A. Nazarov, Ilya A. Shutkov, and Mikhail V. Gorshkov. "On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics." Biochemistry (Moscow) 87, no. 11 (November 2022): 1342–53. http://dx.doi.org/10.1134/s000629792211013x.

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Abstract Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.
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Masood, Afshan, Hicham Benabdelkamel, and Assim Alfadda. "Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity." High-Throughput 7, no. 3 (September 12, 2018): 27. http://dx.doi.org/10.3390/ht7030027.

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Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.
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Oikonomou, Panos, Roberto Salatino, and Saeed Tavazoie. "In vivo mRNA display enables large-scale proteomics by next generation sequencing." Proceedings of the National Academy of Sciences 117, no. 43 (October 9, 2020): 26710–18. http://dx.doi.org/10.1073/pnas.2002650117.

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Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.
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Stubbs, Keith A., and David J. Vocadlo. "Affinity-Based Proteomics Probes; Tools for Studying Carbohydrate-Processing Enzymes." Australian Journal of Chemistry 62, no. 6 (2009): 521. http://dx.doi.org/10.1071/ch09140.

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As more information becomes available through the efforts of high-throughput screens, there is increasing pressure on the three main ‘omic’ fields, genomics, proteomics, and metabolomics, to organize this material into useful libraries that enable further understanding of biological systems. Proteomics especially is faced with two highly challenging tasks. The first is assigning the activity of thousands of putative proteins, the existence of which has been suggested by genomics studies. The second is to serve as a link between genomics and metabolomics by demonstrating which enzymes play roles in specific metabolic pathways. Underscoring these challenges in one area are the thousands of putative carbohydrate-processing enzymes that have been bioinformatically identified, mostly in prokaryotes, but that have unknown or unverified activities. Using two brief examples, we illustrate how biochemical pathways within bacteria that involve carbohydrate-processing enzymes present interesting potential antimicrobial targets, offering a clear motivation for gaining a functional understanding of biological proteomes. One method for studying proteomes that has been developed recently is to use synthetic compounds termed activity-based proteomics probes. Activity-based proteomic profiling using such probes facilitates rapid identification of enzyme activities within proteomes and assignment of function to putative enzymes. Here we discuss the general design principles for these probes with particular reference to carbohydrate-processing enzymes and give an example of using such a probe for the profiling of a bacterial proteome.
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Tjalsma, Harold, Haike Antelmann, Jan D. H. Jongbloed, Peter G. Braun, Elise Darmon, Ronald Dorenbos, Jean-Yves F. Dubois, et al. "Proteomics of Protein Secretion by Bacillus subtilis: Separating the “Secrets” of the Secretome." Microbiology and Molecular Biology Reviews 68, no. 2 (June 2004): 207–33. http://dx.doi.org/10.1128/mmbr.68.2.207-233.2004.

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SUMMARY Secretory proteins perform a variety of important“ remote-control” functions for bacterial survival in the environment. The availability of complete genome sequences has allowed us to make predictions about the composition of bacterial machinery for protein secretion as well as the extracellular complement of bacterial proteomes. Recently, the power of proteomics was successfully employed to evaluate genome-based models of these so-called secretomes. Progress in this field is well illustrated by the proteomic analysis of protein secretion by the gram-positive bacterium Bacillus subtilis, for which ∼90 extracellular proteins were identified. Analysis of these proteins disclosed various“ secrets of the secretome,” such as the residence of cytoplasmic and predicted cell envelope proteins in the extracellular proteome. This showed that genome-based predictions reflect only∼ 50% of the actual composition of the extracellular proteome of B. subtilis. Importantly, proteomics allowed the first verification of the impact of individual secretion machinery components on the total flow of proteins from the cytoplasm to the extracellular environment. In conclusion, proteomics has yielded a variety of novel leads for the analysis of protein traffic in B. subtilis and other gram-positive bacteria. Ultimately, such leads will serve to increase our understanding of virulence factor biogenesis in gram-positive pathogens, which is likely to be of high medical relevance.
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Dissertations / Theses on the topic "Proteome proteomics"

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Ciryam, Prajwal. "Proteome metastability in stress, aging, and disease." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708160.

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McDonald, Lucy. "Positional proteomics : advanced strategies for targeted proteome simplification." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501607.

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Proteome complexity presents a major challenge in the field of proteomics. The majority of bottom-up methods begin with proteolysis, which increases the number of analytes in the mixture by about 30-50 fold. This level of complexity demands simplification, and there is an increasing requirement for strategies and reagents that reduce the complexity of a total proteome mixture. It may be argued that when analysing a complete protein digest, for instance by standard shotgun methods, more peptides are analysed than strictly necessary. An efficient proteomic strategy simplifies the proteome while preserving most of the information necessary for comprehensive analysis. A practical approach to proteome simplification is to target a specific structural region of the protein molecule. The ultimate simplification strategy would be to select a single signature peptide from each protein in the proteome.
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Sun, Jin. "Characterization of the egg and embryonic proteome of Pomacea canaliculata, and responses of the proteome to environmental stressors." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1519.

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Woolerton, Yvonne. "Quantitative proteomics strategies to explore the Saccharomyces cerevisiae proteome." Thesis, University of Liverpool, 2014. http://livrepository.liverpool.ac.uk/2025519/.

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Quantitative proteomics aims at not just identifying, but accurately quantifying the cellular proteome, and while technological advances towards accurate and reliable quantification of proteins is advancing, this alone does not provide an accurate picture of a proteins role within a cell. There is a far greater level of functionality in the cellular environment than there are protein coding genes in the genome, owing partly to the organisation of individual proteins into larger assemblies. A single protein can form interactions with, potentially, a large number of other proteins, leading to a variety of different protein complexes, and subunits can move, break apart or combine depending on cellular conditions. This complex organisation is despite the normal proteomics strategies employing a destructive process, breaking protein structure down to the peptide level. Further difficulties in mapping the cellular proteome arise from the differential expression level of proteins, which in S.cerevisiae can span up to 5 orders of magnitude. This poses problems for the quantification of less abundant proteins in the cell, which can be masked by the more concentrated proteins. An attempt is made within this thesis to use quantitative proteomic techniques to build a picture of the S.cerevisiae cellular proteome. For the analysis of S.cerevisiae protein complexes ion exchange chromatography has been used to separate the cellular proteome into discrete fractions, each containing a different array of protein complexes. The aim here was to analyse the individual subunits of these complexes by LC-MS, with the use of label free quantification strategies. This enables the high throughput identification and quantification of 1800 proteins along with their potential interaction partners. However, for some of the complexes presented here the accuracy of the label free quantification is called into question, as complex subunits known to be equimolar are identified at different concentrations. In order to assess the accuracy of the label free data QconCATs were also designed to analyse the subunits of some complexes by label mediated quantification. In addition, an attempt is made to access proteins from the entire dynamic range of the cellular proteome using equaliser bead technology. This method uses a library of hexapeptide ligands bound to porous beads to bind, theoretically, every protein present in the sample to equal amounts. The beads are used here to bring up the less abundant proteins in the sample, while simultaneously reducing the amount of the abundant proteins. While this goal is achieved, it is also evident that certain proteins are able to bind the beads to a much larger extent than others, so rather than reducing the dynamic range of proteins identified, there is more of a shift in the dynamics, with previously mid-range proteins becoming highly abundant in the data presented here.
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Klammer, Aaron A. "Revealing the proteome : a machine learning approach to peptide identification /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/10278.

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Branson, Owen E. "Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685.

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Yee, Fong-ying Anita, and 伊芳盈. "Transcriptome and proteome of the intervertebral disc in health and disease." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43752354.

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Yee, Fong-ying Anita. "Transcriptome and proteome of the intervertebral disc in health and disease." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B43752354.

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Fisher, Christal. "Quantitative analysis of the plasma proteome in pre-eclampsia." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/quantitative-analysis-of-the-plasma-proteome-in-preeclampsia(3e207341-ebb9-4cb0-b7ea-34b9b110eda6).html.

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There is currently no clinically useful screening test available to identify nulliparous women at high risk of developing pre-eclampsia. This study aimed to identify novel biomarkers using hypothesis generating proteomic methods applied to plasma samples obtained prior to clinical diagnosis of pre-eclampsia. Plasma samples taken at 15 weeks gestation from women who subsequently developed late pre-eclampsia (> 34 weeks), early pre-eclampsia (< 34 weeks) and two distinct groups of women with uncomplicated pregnancies (each n=12) were pooled. Pooled plasma was immunodepleted, labelled using iTRAQ-8 plex reagent and separated into fractions using high pH reverse phase chromatography. Fractions were analysed by LC-MS/MS and data interrogated using ProteinPilot 3.0. The merits of two immunodepletion systems were compared; the Seppro® IgY 14 -SuperMix LC column system removes up to 100 highly abundant plasma proteins and the Multiple Affinity Removal LC column depletes 14 highly abundant plasma proteins. Removal of more high abundance proteins allowed identification of more, potentially interesting, low abundance proteins, but was less reproducible than removing fewer proteins. Two methods of LC-MS/MS analysis were assessed; the QStar XL qTOF and 5800 MALDI-TOF-TOF. The protein identifications and the quantification data acquired by each method was comparable and complementary and increased the total number of proteins identified. A total of 502 proteins were identified. A stringent two stage analysis was developed to identify candidate proteins which changed in abundance in plasma from women who later developed pre-eclampsia compared to women with uncomplicated pregnancies. Analysis identified a total of 113 proteins which were both reproducibly quantified and changed by more than the expected range of biological variation. Six candidate proteins changed in abundance in the plasma taken from women who subsequently developed early pre-eclampsia were selected for further validation. A high throughput, low cost, method of multiple reaction monitoring which allows relative quantitation without the use of costly isotopically labelled peptides was developed to validate candidate proteins. Candidate proteins were also assessed by western blot and ELISA. Only one candidate protein; platelet basic protein, was validated by all three methods and demonstrated similar increases in the abundance. This investigation suggests that measurement of platelet basic protein at 15 weeks gestation is a novel candidate predictive marker for pre-eclampsia. Validation of platelet basic protein in a large, independent, sample set is required to confirm changes in protein expression and to evaluate potential, alongside other factors, to identify nulliparous women at high risk of developing pre-eclampsia later in pregnancy.
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Tabb, David L. "Bioinformatics of proteomic tandem mass spectra : selection, characterization, and identification /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10847.

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Books on the topic "Proteome proteomics"

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Proteome bioinformatics. New York, NY: Humana, 2010.

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Mishra, Nawin C. Introduction to proteomics: Principles and applications. Hoboken, N.J: Wiley, 2010.

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Mishra, Nawin C. Introduction to proteomics: Principles and applications. Hoboken, N.J: John Wiley & Sons, 2010.

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1951-, Kamp R. M., Calvete Juan J, and Choli-Papadopoulou T. 1956-, eds. Methods in proteome and protein analysis. New York: Springer-Verlag, 2004.

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The urinary proteome: Methods and protocols. New York, NY: Humana Press, 2010.

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Introduction to proteomics: Principles and applications. Hoboken, N.J: Wiley, 2010.

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Tom, Naven, ed. Proteomics in practice: A laboratory manual of proteome analysis. Weinheim: Wiley-VCH, 2002.

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S, Omenn Gilbert, ed. Exploring the human plasma proteome. Weinheim: Wiley-VCH, 2006.

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D, Pagel Mark, and Pomiankowski Andrew, eds. Evolutionary genomics and proteomics. Sunderland, Mass: Sinauer Associates, 2008.

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Tzortzatou, Stathopoulou Fotini, ed. Genome and proteome in oncology. New York: Nova Science Publishers, 2005.

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Book chapters on the topic "Proteome proteomics"

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology, 1351. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_1289.

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology, 2037–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44185-5_1289.

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27833-4_1289-3.

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Sanchez, Jean-Charles, Yohann Couté, Laure Allard, Pierre Lescuyer, and Denis F. Hochstrasser. "Biomedical Applications of Proteomics." In Proteome Research, 193–221. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72910-5_9.

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Langen, Hanno, and Peter Berndt. "Proteomics Databases." In Proteome Research: Mass Spectrometry, 229–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56895-4_12.

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Minic, Zoran, Georges Boudart, Cécile Albenne, Hervé Canut, Elisabeth Jamet, and Rafael F. Pont-Lezica. "Cell Wall Proteome." In Plant Proteomics, 169–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72617-3_12.

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Sénécaut, Nicolas, Pierre Poulain, Laurent Lignières, Samuel Terrier, Véronique Legros, Guillaume Chevreux, Gaëlle Lelandais, and Jean-Michel Camadro. "Quantitative Proteomics in Yeast: From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores." In Methods in Molecular Biology, 275–92. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_16.

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AbstractSimple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476–1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
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Bendixen, Emøke. "Understanding the Proteome." In Proteomics in Foods, 3–19. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5626-1_1.

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Senis, Yotis A. "The Platelet Membrane Proteome." In Platelet Proteomics, 111–37. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940297.ch5.

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Smalley, David M. "The Platelet Microparticle Proteome." In Platelet Proteomics, 159–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940297.ch7.

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Conference papers on the topic "Proteome proteomics"

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Bandeira, Nuno. "Revealing deep proteome diversity with community-scale proteomics big data." In CSBio '17: 8th International Conference on Computational Systems-Biology and Bioinformatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3156346.3156694.

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Pavlou, MP, AP Drabovich, A. Dimitromanolakis, and EP Diamandis. "Abstract P6-05-09: Unravelling the global effect of estrogen on breast cancer cell proteome using quantitative proteomics." In Abstracts: Thirty-Fifth Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 4‐8, 2012; San Antonio, TX. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/0008-5472.sabcs12-p6-05-09.

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Vowinckel, Jakob, Karel Novy, Thomas Corwin, Tobias Treiber, Roland Bruderer, Lukas Reiter, Eike von Leitner, Oliver Rinner, and Claudia Escher. "Abstract 4266: Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4266.

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Lord, RW, RE Maher, V. Harman, B. Bianco, PJ Whorwell, PS McNamara, JA Smith, RJ Beynon, and AM Jones. "S18 The sputum proteome and its relationship to cystic fibrosis lung disease: using global proteomics to develop clinically useful biomarkers." In British Thoracic Society Winter Meeting 2019, QEII Centre, Broad Sanctuary, Westminster, London SW1P 3EE, 4 to 6 December 2019, Programme and Abstracts. BMJ Publishing Group Ltd and British Thoracic Society, 2019. http://dx.doi.org/10.1136/thorax-2019-btsabstracts2019.24.

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Selvam, Anjan Panneer, and Shalini Prasad. "Single Molecule Analysis Tool (SMAT) for Multiplexed Label-Free Assessment of Rare Cell Populations." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-40225.

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A nanowell sensor for single molecular proteomic analysis of lung cancer has been designed. The nanowell sensor is an electrochemical immunoassay and comprises of a heterogenous nanoporous arrays integrated on to a gold microelectronic platform. The sensor operates on the principle of electrochemical impedance spectroscopy (EIS). Our approach to classification of lung cancer is based on screening for levels of expression of specific proteomic biomarkers associated with lung cancer stem cells. Proteomic activity for two lung cancer cell lines for two specific markers (ALDH1A1 and ALDH1A3) was quantified. Test samples prepared by synthetically spiking human pooled serum were tested and quantified for cancer stem cell marker activity. The lowest proteomic activity measured with (a) ALDH1A3 was 0.01 ng/mL and (b) ALDH1A1 was 1 ng/mL correlating to the detection of unit stem cell count.
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Costessi, Mr Adalberto, Mr Carlo Vascotto, Dr Alex Pines, Mr Rogier Schonenborg, Dr Milena Romanello, Dr Peter Schiller, Prof Luigi Moro, and Prof Gianluca Tell. "Bone Proteomics experiment (BOP): the first proteomic analysis of mammalian cells cultured in weightlessness conditions." In 57th International Astronautical Congress. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.iac-06-a1.4.08.

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Martens, William L., Philip Poronnik, and Darren Saunders. "Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis." In The 22nd International Conference on Auditory Display. Arlington, Virginia: The International Community for Auditory Display, 2016. http://dx.doi.org/10.21785/icad2016.024.

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Three alternative sonifications of proteomic data distributions were compared as a means to indicate the neuropathology associated with Amyotrophic Lateral Sclerosis (ALS) via auditory display (through exploration of the differentiation of induced pluripotent stem cell derived neurons). Pure visual displays of proteomic data often result in ”visual overload” such that detailed or subtle data important to describe ALS neurodegradation may be glossed over, and so three competing approaches to the sonification of proteomic data were designed to capitalize upon human auditory capacities that complement the visual capacities engaged by more conventional graphic representations. The auditory displays resulting from hypothesis-driven design of three alternative sonifications were evaluated by naïve listeners, who were instructed to listen for differences between the sonifications produce from proteomic data associated with three different types of cells. One of the sonifications was based upon the hypothesis that auditory sensitivity to regularities and irregularities in spatio-temporal patterns in the data could be heard through spatial distribution of sonification components. The design of a second sonification was based upon the hypothesis that variation in timbral components might create a distinguishable sound for each of three types of cells. A third sonification was based upon the hypothesis that redundant variation in both spatial and timbral components would be even more powerful as a means for identifying spatio-temporal patterns in the dynamic, multidimensional data generated in current proteomic studies of ALS.
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Schmitt, H., A. Warnecke, I. De Vries, T. Lenarz, S. Alvi, N. Prenzler, M. Durisin, and H. Staecker. "Charakterisierung des Proteoms humaner Perilymphe mit dem Focus auf BDNF regulierte Proteine." In Abstract- und Posterband – 89. Jahresversammlung der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie e.V., Bonn – Forschung heute – Zukunft morgen. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1640584.

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Tirloni, Lucas. "Proteomic characterization ofRhipicephalus microplussaliva." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.111459.

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Gnabasik, David, and Gita Alaghband. "Topological analysis of proteomic data." In 2012 International Conference on Collaboration Technologies and Systems (CTS). IEEE, 2012. http://dx.doi.org/10.1109/cts.2012.6261006.

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Reports on the topic "Proteome proteomics"

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Dahlgren, Kelsey, Colin Gates, Thomas Lee, and Jeffrey Cameron. Proximity-based proteomics reveals the thylakoid lumen proteome in the cyanobacterium Synechococcus sp. PCC 7002. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1900233.

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Ghanim, Murad, Joe Cicero, Judith K. Brown, and Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, February 2010. http://dx.doi.org/10.32747/2010.7592654.bard.

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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that interfere with whitefly-mediated begomovirus transmission. The project objectives were to: 1) Perform gene expression analyses using microarrays to study the response of whiteflies (B, Q and A biotypes) to the acquisition of begomoviruses (Tomato yellow leaf curl (TYLCV) and Squash leaf curl (SLCV). 2) Construct a whitefly proteome from whole whiteflies and dissected organs after begomovirus acquisition. 3) Validate gene expression by q-RTPCR and sub-cellular localization of candidate ESTs identified in microarray and proteomic analyses. 4) Verify functionality of candidate ESTs using an RNAi approach, and to link these datasets to overall functional whitefly anatomical studies. During the first and second years biological experiments with TYLCV and SLCV acquisition and transmission were completed to verify the suitable parameters for sample collection for microarray experiments. The parameters were generally found to be similar to previously published results by our groups and others. Samples from whole whiteflies and midguts of the B, A and Q biotypes that acquired TYLCV and SLCV were collected in both the US and Israel and hybridized to B. tabaci microarray. The data we analyzed, candidate genes that respond to both viruses in the three tested biotypes were identified and their expression that included quantitative real-time PCR and co-localization was verified for HSP70 by the Israeli group. In addition, experiments were undertaken to employ in situ hybridization to localize several candidate genes (in progress) using an oligonucleotide probe to the primary endosymbiont as a positive control. A proteome and corresponding transcriptome to enable more effective protein identification of adult whiteflies was constructed by the US group. Further validation of the transmission route of begomoviruses, mainly SLCV and the involvement of the digestive and salivary systems was investigated (Cicero and Brown). Due to time and budget constraints the RNAi-mediated silencing objective to verify gene function was not accomplished as anticipated. HSP70, a strong candidate protein that showed over-expression after TYLCV and SLCV acquisition and retention by B. tabaci, and co-localization with TYLCV in the midgut, was further studies. Besides this protein, our joint research resulted in the identification of many intriguing candidate genes and proteins that will be followed up by additional experiments during our future research. To identify these proteins it was necessary to increase the number and breadth of whitefly ESTs substantially and so whitefly cDNAs from various libraries made during the project were sequenced (Sanger, 454). As a result, the proteome annotation (ID) was far more successful than in the initial attempt to identify proteins using Uniprot or translated insect ESTs from public databases. The extent of homology shared by insects in different orders was surprisingly low, underscoring the imperative need for genome and transcriptome sequencing of homopteran insects. Having increased the number of EST from the original usable 5500 generated several years ago to >600,000 (this project+NCBI data mining), we have identified about one fifth of the whitefly proteome using these new resources. Also we have created a database that links all identified whitefly proteins to the PAVEdb-ESTs in the database, resulting in a useful dataset to which additional ESTS will be added. We are optimistic about the prospect of linking the proteome ID results to the transcriptome database to enable our own and other labs the opportunity to functionally annotate not only genes and proteins involved in our area of interest (whitefly mediated transmission) but for the plethora of other functionalities that will emerge from mining and functionally annotating other key genes and gene families in whitefly metabolism, development, among others. This joint grant has resulted in the identification of numerous candidate proteins involved in begomovirus transmission by B. tabaci. A next major step will be to capitalize on validated genes/proteins to develop approaches to interfere with the virus transmission.
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Hayward, Simon W. Therapy Selection by Proteomic Profiling. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada502570.

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Hayward, Simon W. Therapy Selection by Proteomic Profiling. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada435061.

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Lamartiniere, Coral A. Proteomic Analysis of Genistein Mammary Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada442989.

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Lipton, Mary. Identification of Metal Reductases using Proteomic Analysis. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/896018.

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Wilson, Michael J. Proteomic Analysis of Prostate Cancer Field Effect. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada497256.

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Lamartiniere, Coral A. Proteomic Analysis of Genistein Mammary Cancer Chemoprevention. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada428933.

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Anderson, Mark J., Trisha Grevengoed, Steven M. Lonergan, and Elisabeth J. Huff-Lonergan. Proteomic Analysis of Bovine Muscles during Aging. Ames (Iowa): Iowa State University, January 2010. http://dx.doi.org/10.31274/ans_air-180814-1244.

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Turchi, John J. Proteomic Analysis of Cisplatin-Resistant Ovarian Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada425620.

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