Relevant bibliographies by topics / Proteomic

Contents

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

Academic literature on the topic 'Proteomic'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 November 2024

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Journal articles on the topic "Proteomic"

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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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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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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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Agarwal, Ashok, Manesh Kumar Panner Selvam, and Saradha Baskaran. "Proteomic Analyses of Human Sperm Cells: Understanding the Role of Proteins and Molecular Pathways Affecting Male Reproductive Health." International Journal of Molecular Sciences 21, no. 5 (February 27, 2020): 1621. http://dx.doi.org/10.3390/ijms21051621.

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Human sperm proteomics research has gained increasing attention lately, which provides complete information about the functional state of the spermatozoa. Changes in the sperm proteome are evident in several male infertility associated conditions. Global proteomic tools, such as liquid chromatography tandem mass spectrometry and matrix-assisted laser desorption/ionization time-of-flight, are used to profile the sperm proteins to identify the molecular pathways that are defective in infertile men. This review discusses the use of proteomic techniques to analyze the spermatozoa proteome. It also highlights the general steps involved in global proteomic approaches including bioinformatic analysis of the sperm proteomic data. Also, we have presented the findings of major proteomic studies and possible biomarkers in the diagnosis and therapeutics of male infertility. Extensive research on sperm proteome will help in understanding the role of fertility associated sperm proteins. Validation of the sperm proteins as biomarkers in different male infertility conditions may aid the physician in better clinical management.
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Zheng, Ying, Lindsay Olita, McIver Nivens, Abby Chiang, Shreya Ahuja, Matthew Glover, John Bullen, Elaine Hurt, and Wenyan Zhong. "Abstract 2285: Mass spectrometry-based proteomics profiling reveals differential proteome composition in tumor and normal tissues, with implications for normalization." Cancer Research 84, no. 6_Supplement (March 22, 2024): 2285. http://dx.doi.org/10.1158/1538-7445.am2024-2285.

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Abstract The composition of the proteome varies significantly across different tissues and disease states creating a challenge of how best to compare expression levels of each protein across these tissue types. Traditionally, a median approach to normalization has been used, but this could obscure meaningful biological differences between tissues. Here, we present a quantitative proteomic study analyzing proteome composition across tumor and normal tissues in order to assess normalization methods. Human proteomes covering 7 cancer indications (244 samples) and 12 normal tissue organs (370 samples) were analyzed by data-independent acquisition mass spectrometry. On average, over 8,000 proteins were quantified across the study cohorts. Comparison of proteomes revealed an average of 11.4% higher protein identification in tumors compared to matched normal tissues (6 tumor types all p<0.003). Tissue origin strongly influenced proteome composition, most notably in bone marrow, while cancer tissues displayed less proteomic composition variation than normal tissues. Compared to global median normalization, riBAQ and tissue-specific quantile normalization reduced technical variances while maintaining biological differences between tissue types. By revealing tissue-specific proteome signatures as well as systemic proteomic alterations in tumors, this study lays the groundwork for appropriate comparative approaches accounting for tissue origins, and establishes a framework for discovery of tissue-specific biomarkers, which may facilitate the development of novel diagnostic and prognostic tests, and guide therapeutic interventions for cancer. Citation Format: Ying Zheng, Lindsay Olita, McIver Nivens, Abby Chiang, Shreya Ahuja, Matthew Glover, John Bullen, Elaine Hurt, Wenyan Zhong. Mass spectrometry-based proteomics profiling reveals differential proteome composition in tumor and normal tissues, with implications for normalization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2285.
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Lapinel, Nicole, Jessie Guidry, Mary Varkey, Manish Rijal, Arnold Zea, and Juzar Ali. "76215 Implementation of Proteomics as a Diagnostic tool for Nontuberculous mycobacteria (NTM) Infection." Journal of Clinical and Translational Science 5, s1 (March 2021): 140–41. http://dx.doi.org/10.1017/cts.2021.759.

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ABSTRACT IMPACT: Implementation of proteomics as a diagnostic tool for Nontuberculous mycobacteria (NTM) infection can provide a more accurate, efficient and cost-effective means for effectively diagnosing disease and enacting timely management decisions which can revolutionize patient care. OBJECTIVES/GOALS: Proteomic analysis is a proven diagnostic modality enabling rapid identification of microorganisms. We sought to apply proteomics to detect proteins unique to the most clinically relevant NTM. We then determined whether these unique proteomes could be used to successfully identify NTM species from in vitro cocktail preparations. METHODS/STUDY POPULATION: NTM reference strains for M. avium, m. intracellulare, m.chimaera, m. abscessus abscessus, m. abscessus massiliense and m. abscessus boletti were cultured in vitro and subjected to proteomic analysis using Liquid Chromatography tandem-Mass Spectrometry (LCMS). Tandem Mass Tag (TMT) data acquisition utilized an MS3 approach for data collection using Proteome Discoverer 2.4.A comparative analysis of the proteome of each of these six species was performed quantitatively using LCMS. The process was repeated for three technical replicates and analyzed using the SEQUEST algorithm. Only high scoring peptides were considered utilizing a false discovery rate (FDR) of 1%. Once species-specific proteins were identified, we validated detection in individual and mixed samples of the six reference strains. RESULTS/ANTICIPATED RESULTS: The proteomic profiling of the six NTM reference strains successfully demonstrated proteins unique to each of the MAC species and MABC subspecies. Proteomic MAC species analysis produced between 327 to 2,540 unique peptides for each of the 3 species. MABC proteomic analysis identified between 17-74 unique peptides for each of the 3 subspecies. Fifteen different mixed preparations of MAC and MABC were then subjected to LCMS analysis and compared against the proteome profiles already curated for the six strains. We accurately identified at least one NTM in the majority of the samples (10/15). In three samples (3/15), the NTM was not correctly identified; in two of the samples (2/15) we were unable to determine the identity of NTM within the preparation. Further database curation will be performed to hone these results. DISCUSSION/SIGNIFICANCE OF FINDINGS: Proteomic analysis of in vitro reference strains successfully demonstrated protein fingerprints specific to six common disease-causing strains of NTM. Such findings can be used to evaluate clinical samples enabling more efficient diagnostic specificity. Further research will focus on identification of NTM in sputum samples of infected patients.
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Hulahan, Taylor S., Laura Spruill, Elizabeth N. Wallace, Yeonhee Park, Robert B. West, Jeffrey R. Marks, E. Shelley Hwang, Richard R. Drake, and Peggi M. Angel. "Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics." International Journal of Molecular Sciences 25, no. 12 (June 19, 2024): 6748. http://dx.doi.org/10.3390/ijms25126748.

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Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.
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Yaacob, Mohamad Fakhri, Nur Anisah Johari, Alya Nur Athirah Kamaruzzaman, and Mohd Fakharul Zaman Raja Yahya. "Mass Spectrometry-Based Proteomic Investigation of Heterogeneous Biofilms: A Review." Scientific Research Journal 18, no. 2 (September 1, 2021): 67–87. http://dx.doi.org/10.24191/srj.v18i2.11718.

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Biofilm represents a major public health concern. It is a highly structured and heterogeneous microbial population that is well protected by a hydrated extracellular matrix. In most cases, the difficulties in combating a wide spectrum of biofilm-associated diseases are due to the presence of dormant cells and differential molecular expression. Proteomics is the large-scale and systematic study of cellular proteome expression at any given time by mass spectrometry. It allows high-sensitivity and high-specificity identification of differentially expressed proteins in the biofilms. Over the past few decades, multiple lines of proteomic works have successfully elucidated various aspects of the biofilm including developmental stages, antimicrobial resistance, and survival mechanisms. However, the heterogeneity of biofilms may contribute to inconsistent proteome expression throughout a proteomic experiment. This is due to the fact that the mature biofilm is often associated with the mixture between monolayer and multilayer biofilms, thick microbial population, and chemical gradient of nutrients. This review highlights the biofilm heterogeneities, the principle of mass spectrometry in proteomics, and the possible strategies for quantitative proteomic analysis of heterogeneous biofilms. It is suggested that isolation of monolayer biofilm, laser capture microdissection, flow cytometry, and subtractive proteome profiling may be considered for an accurate and reliable quantitative proteomics experiment.
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Bespyatykh, Ju A., E. A. Shitikov, and E. N. Ilina. "Proteomics for the Investigation of Mycobacteria." Acta Naturae 9, no. 1 (March 15, 2017): 15–25. http://dx.doi.org/10.32607/20758251-2017-9-1-15-25.

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The physiology of Mycobacterium tuberculosis, the causative agent of tuberculosis, is being studied with intensity. However, despite the genomic and transcriptomic data available today, the pathogenic potential of these bacteria remains poorly understood. Therefore, proteomic approaches seem relevant in studying mycobacteria. This review covers the main stages in the proteomic analysis methods used to study mycobacteria. The main achievements in the area of M. tuberculosis proteomics are described in general. Special attention is paid to the proteomic features of the Beijing family, which is widespread in Russia. Considering that the proteome is a set of all the proteins in the cell, post-translational modifications of mycobacterium proteins are also described.
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Dissertations / Theses on the topic "Proteomic"

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Lang, Alastair Michael. "Developing tissue proteomics : differential in gel electrophoresis in biomarker discovery and proteomic degradation." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4642/.

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The field of proteomics and functional genomics has developed steadily since the completion of the human genome project. The wealth of genomic information and the pace at which it was compiled was astounding. Proteomics, despite considerable effort, on the other hand has not seen quite the same pace of development. The progress being considerably hindered by the lack of an amplification process and the relative complexity of the proteome in comparison to the genome. These intrinsic difficulties have led to the sensitivity of proteomic techniques being pushed closer to physical limits. There is therefore a further need to re-evaluated techniques such as sample preparation and integrity, analytical methods and collaborative strategies to maximise the effectiveness and quality of data collected. The importance of tissue in scientific and clinical research is unequivocal. However, tissue is difficult to collect, store and work with due to issues with proteomic degradation and storage. Good lab practices can minimise the effect of degradation but degradation of proteins can be rapid. Strategies to minimise degradation include freezing, formalin fixing and microwave treatment which all have their relative advantages and disadvantages. The importance of sample preparation as being the top of the workflow is often acknowledged but improvements are not well described in the literature. The main aim of this thesis is to present investigative studies into the mitigation of some of the limitations in tissue sample degradation, analytical approaches in differential in gel electrophoresis and accessing DiGE spot and tissue profile data. Presented is the evaluation of the effectiveness of rapid and controlled heating of intact tissue to inactivate native enzymatic activity and to aid in the cessation of proteomic degradation. A multifaceted analytical approach of differential in Gel electrophoresis spot data is assessed, giving proteomic profiles of mouse brain tissue. Preliminary data is presented showing that the process of heat-treatment has had a predominantly beneficial effect on mouse brain tissue, with a higher percentage of spots stabilised in heat-treated samples compared to snap-frozen samples. However, stabilisation did occur in snap-frozen samples for different protein spot so the appropriateness of using heat-treatment is as yet not fully determined and requires further analysis. In addition, the variation in tissue profiles of WKY, SP.WKYGla.2a and SHRSP rat model for hypertension is investigated with the future prospect of providing that vital connection between genomic and proteomic data and link phenotype and genotype preliminary investigated. A number of putative markers were identified and quantified using DiGE analysis. In order for these markers to be accepted as biomarkers, more downstream validation is required, however this study provides a good spring board as a proof of concept in using DiGE as an global putative biomarker discovery platform.
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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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Vogel, Martin Joseph. "Proteomic profiling following cryopreservation." Diss., Online access via UMI:, 2004. http://wwwlib.umi.com/dissertations/fullcit/1424168.

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Jufvas, Åsa. "Human Adipocytes : Proteomic Approaches." Doctoral thesis, Linköpings universitet, Avdelningen för cellbiologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125907.

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Type 2 diabetes is characterized by increased levels of glucose in the blood originating from insulin resistance in insulin sensitive tissues and from reduced pancreatic insulin production. Around 400 million people in the world are diagnosed with type 2 diabetes and the correlation with obesity is strong. In addition to life style induction of obesity and type 2 diabetes, there are indications of genetic and epigenetic influences. This thesis has focused on the characterization of primary human adipocytes, who play a crucial role in the development of type 2 diabetes. Histones are important proteins in chromatin dynamics and may be one of the factors behind epigenetic inheritance. In paper I, we characterized histone variants and posttranslational modifications in human adipocytes. Several of the specific posttranslational histone modifications we identified have been characterized in other cell types, but the majority was not previously known. Moreover, we identified a variant of histone H4 on protein level for the first time. In paper II, we studied specific histone H3 methylations in the adipocytes. We found that overweight is correlated with a reduction of H3K4me2 while type 2 diabetes is associated with an increase of H3K4me3. This shows a genome-wide difference in important chromatin modifications that could help explain the epidemiologically shown association between epigenetics and metabolic health. Caveolae is a plasma membrane structure involved in the initial and important steps of insulin signaling. In paper III we characterized the IQGAP1 interactome in human adipocytes and suggest that IQGAP1 is a link between caveolae and the cytoskeleton. Moreover, the amount of IQGAP1 is drastically lower in adipocytes from type 2 diabetic subjects compared with controls implying a potential role for IQGAP1 in insulin resistance. In conclusion, this thesis provides new insights into the insulin signaling frameworks and the histone variants and modifications of human adipocytes.
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Le, Thi Tam. "Proteomic signatures of Bacillus subtilis." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=984429247.

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Schiess, Ralph. "Proteomic strategy for biomarker discovery /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=18097.

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Mujahid, Sana. "PROTEOMIC ANALYSIS OF LISTERIA MONOCYTOGENES." MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11012007-174823/.

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Listeria monocytogenes is a deadly, Gram-positive foodborne pathogen that is ubiquitous in the environment. The bacterium expresses a number of virulence and stress adaptation proteins that support its pathogenic capabilities. Two-dimensional gel electrophoresis (2-DE) was used to map L. monocytogenes surface proteins, which play a central role in virulence, and to examine protein expression by L. monocytogenes grown on ready-to-eat meat, an important source of Listeria infections. A novel method for solubilization of surface proteins from L. monocytogenes for 2-DE was developed. Additionally, the unique proteome expressed by L. monocytogenes grown on a meat matrix was uncovered. The developed solubilization method will facilitate efforts to identify and routinely compare surface proteins of Listeria by 2-DE. Furthermore, the 2-DE database of proteins expressed by L. monocytogenes grown on a meat matrix will allow further understanding of the interactions of Listeria with its food environment that influence its ability to cause disease.
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Mi, Jia. "Proteomic Analysis of Peroxisomal Proteins." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7943.

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Chen, R. "Proteomic study of mitochondrial proteins." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597549.

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Several proteins in bovine heart mitochondria with molecular masses of 29, 18 and 10 kDa have been demonstrated to be phosphorylated in a cAMP-dependent manner. The 18 kDa and 10 kDa proteins are subunits of complex I. In the presence of protein kinase A, subunits of purified complex I with the same molecular masses are phosphorylated in vitro. By Edman degradation and mass spectrometry of the radioactive protein bands from 32P-phosphorylated mitochondria and 32P-phosphorylated complex I, the 18kDa protein has been identified as subunit ESSS of complex I. It is phosphorylated at serine-20. In the 10 kDa band, subunit MWFE of complex I and subunit d of complex II are phosphorylated at serine-55 and serine-7, respectively. Five distinct 14-3-3 proteins have been detected in bovine heart mitochondria by immunological methods and by mass spectrometry. They are the b, g, e, h and ζ isoforms of the protein. Recombinant 14-3-3 b, g and ζ proteins have been over-expressed in Escherichia coli, and purified to homogeneity. The hydrophobic membrane protein, subunit c, has been isolated from bovine heart mitochondria, and from ovine and human lysosomal storage bodies associated with ceroid lipofuscinosis (Batten disease). By mass spectrometry, a post-translational modification with a mass of 42 Da is associated with a chymotryptic fragment of this protein in all three samples. By tandem MS sequencing, it has been shown that lysine-43 is trimethylated, and not acetylated, at the e-N-position of the residue is subunit c from all three samples. Therefore, trimethylation of subunit c is not, as has been suggested, a cause of abnormal accumulation of the subunit in lysosomes. This modification is not found in the equivalent lysine-44 of subunit c from yeast mitochondria. Therefore, the role of the modified lysine-43 in the assembly and (or) the functioning of the mitochondrial ATP synthase complex appears to be confined to higher organisms.
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Zhang, Meng. "Proteomic analysis of streptococcus pyogenes." Thesis, Northumbria University, 2007. http://nrl.northumbria.ac.uk/842/.

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Streptococcus pyogenes (group A streptococcus, GAS) is a major human Gram-positive pathogen that causes infections that normally occur in the respiratory tract, the skin, the wound, the lung, the bloodstream and/or muscle tissues and result in millions of deaths every year. To cause such infections, S. pyogenes produces a wide range of virulence factors. The destruction of connective tissue and the hyaluronic acid therein plays an important role in pathogenesis. S. pyogenes was propagated in hyaluronic acid rich growth media in an attempt to create a simple biological system that could reflect some elements of the pathogenesis. The growth of bacteria was analyzed in the hyaluronic acid rich media and control media and a proteomic approach was applied to identify those proteins that were differentially expressed by the streptococcal pathogens growing in the different media. The techniques of two dimensional gel electrophoresis and static nanospray mass spectrometry were optimized and proteome maps for S. pyogenes grown in both media were constructed. The differentially expressed proteins by S. pyogenes were identified and analyzed using bioinformatics. Our results showed that several recognized virulence factors of S. pyogenes were upregulated in hyaluronic acid rich media, including the Ml protein, a collagen-like surface protein and the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase, which has been shown to play important roles in streptococcal pathogenesis. Interestingly, two hypothetical proteins of unknown function were also up-regulated and detailed bioinformatics analysis showed that at least one of these hypothetical proteins is likely to be involved in GAS pathogenesis. It was therefore concluded that this simple biological system provided a valuable tool for the identification of potential streptococcal pathogens virulence factors.
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Books on the topic "Proteomic"

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Posch, Anton, ed. Proteomic Profiling. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2550-6.

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Posch, Anton, ed. Proteomic Profiling. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1186-9.

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Conn, P. Michael. Handbook of Proteomic Methods. New Jersey: Humana Press, 2003. http://dx.doi.org/10.1385/159259414x.

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Conn, P. Michael, ed. Handbook of Proteomic Methods. Totowa, NJ: Humana Press, 2003. http://dx.doi.org/10.1007/978-1-59259-414-6.

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name, No. Handbook of proteomic methods. Totowa, NJ: Humana Press, 2003.

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Michael, Conn P., ed. Handbook of proteomic methods. Totowa, NJ: Humana Press, 2003.

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Dobrin, Nedelkov, and Nelson W. Randall. New and Emerging Proteomic Techniques. New Jersey: Humana Press, 2006. http://dx.doi.org/10.1385/159745026x.

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Guest, Paul C., ed. Proteomic Methods in Neuropsychiatric Research. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52479-5.

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Burger, Thomas, ed. Statistical Analysis of Proteomic Data. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-1967-4.

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Dobrin, Nedelkov, and Nelson Randall W, eds. New and emerging proteomic techniques. Totowa, N.J: Humana Press, 2006.

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Book chapters on the topic "Proteomic"

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Schork, Karin, Katharina Podwojski, Michael Turewicz, Christian Stephan, and Martin Eisenacher. "Important Issues in : Statistical Considerations of Quantitative Proteomic Data." In Methods in Molecular Biology, 1–20. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_1.

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AbstractMass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.
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Debnath, Mousumi, Godavarthi B. K. S. Prasad, and Prakash S. Bisen. "Proteomic Technology." In Molecular Diagnostics: Promises and Possibilities, 245–59. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3261-4_16.

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Mias, George. "Proteomic Data." In Mathematica for Bioinformatics, 227–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72377-8_7.

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Cosette, Pascal, and Thierry Jouenne. "Proteomic Analysis." In Methods in Molecular Biology, 205–12. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0473-0_17.

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Pedrioli, Patrick G. A. "Trans-Proteomic Pipeline: A Pipeline for Proteomic Analysis." In Methods in Molecular Biology, 213–38. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-444-9_15.

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Nicora, Carrie, Marina Gritsenko, Anna Lipton, Karen L. Wahl, and Kristin E. Burnum-Johnson. "Proteomic Sample Preparation Techniques: Toward Forensic Proteomic Applications." In ACS Symposium Series, 29–46. Washington, DC: American Chemical Society, 2019. http://dx.doi.org/10.1021/bk-2019-1339.ch003.

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Rees, Johanna S., and Kathryn S. Lilley. "Comparative Proteomic Approaches." In Methods in Animal Proteomics, 121–58. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9780470960660.ch6.

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Hassanein, Mohamed, and David P. Carbone. "Serum Proteomic Biomarkers." In Lung Cancer, 90–109. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118468791.ch5.

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Ji, Xianling, and Yingping Gai. "Phytoplasma Proteomic Analysis." In Methods in Molecular Biology, 339–49. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-089-2_29.

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Rydén, Martin, and Patrik Önnerfjord. "In Vitro Models and Proteomics in Osteoarthritis Research." In Advances in Experimental Medicine and Biology, 57–68. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25588-5_4.

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AbstractThis review summarizes and exemplifies the current understanding of osteoarthritis in vitro models and describes their relevance for new insights in the future of osteoarthritis research. Our friend and highly appreciated colleague, Prof. Alan Grodzinsky has contributed greatly to the understanding of jointtissue biology and cartilage biomechanics. He frequently utilizes in vitro models and cartilage explant cultures, and recent work also includes proteomics studies. This review is dedicated to honor his 75-year birthday and will focus on recent proteomic in vitro studies related to osteoarthritis, and within this topic highlight some of his contributions to the field.
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Conference papers on the topic "Proteomic"

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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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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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Pollinzi, Angela, and Micheline Piquette-Miller. "Proteomic Characterization of the Placental Proteome in Pregnant Systemic Lupus Erythematosus Patients." In ASPET 2024 Annual Meeting Abstract. American Society for Pharmacology and Experimental Therapeutics, 2024. http://dx.doi.org/10.1124/jpet.434.975830.

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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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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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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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Gnabasik, David, and Gita Alaghband. "Discrete Time Evolution of Proteomic Biomarkers." In 2014 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2014. http://dx.doi.org/10.1109/csci.2014.87.

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Liu, Hongbiao, and Juan Liu. "Mining Protein Pathway from Proteomic Data." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.90.

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Miguel, A. C., J. F. Keane, J. Whiteaker, Heidi Zhang, and A. Paulovich. "Compression of LC/MS Proteomic Data." In Proceedings. 19th IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2006. http://dx.doi.org/10.1109/cbms.2006.2.

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PRATAPA, PALLAVI N., EDWARD F. PATZ, and ALEXANDER J. HARTEMINK. "FINDING DIAGNOSTIC BIOMARKERS IN PROTEOMIC SPECTRA." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701626_0026.

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Reports on the topic "Proteomic"

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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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Turchi, John. Proteomic Analysis of Cisplatin-Resistant Ovarian Concers. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada462560.

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Turchi, John J. Proteomic Analysis of Cisplatin-Resistant Ovarian Concers. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada463194.

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