Academic literature on the topic 'Quantitative proteomics data'
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Journal articles on the topic "Quantitative proteomics data"
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Beynon, Rob, Simon Hubbard, and Andy Jones. "Quantitative proteomics and data analysis." Biochemist 34, no. 1 (February 1, 2012): 61–62. http://dx.doi.org/10.1042/bio03401061.
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Handler, David C. L., Flora Cheng, Abdulrahman M. Shathili, and Paul A. Haynes. "PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data." Proteomes 8, no. 3 (August 21, 2020): 21. http://dx.doi.org/10.3390/proteomes8030021.
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PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.
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Held, Jason M., Birgit Schilling, Alexandria K. D'Souza, Tara Srinivasan, Jessica B. Behring, Dylan J. Sorensen, Christopher C. Benz, and Bradford W. Gibson. "Label-Free Quantitation and Mapping of the ErbB2 Tumor Receptor by Multiple Protease Digestion with Data-Dependent (MS1) and Data-Independent (MS2) Acquisitions." International Journal of Proteomics 2013 (April 4, 2013): 1–11. http://dx.doi.org/10.1155/2013/791985.
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The receptor tyrosine kinase ErbB2 is a breast cancer biomarker whose posttranslational modifications (PTMs) are a key indicator of its activation. Quantifying the expression and PTMs of biomarkers such as ErbB2 by selected reaction monitoring (SRM) mass spectrometry has several limitations, including minimal coverage and extensive assay development time. Therefore, we assessed the utility of two high resolution, full scan mass spectrometry approaches, MS1 Filtering and SWATH MS2, for targeted ErbB2 proteomics. Endogenous ErbB2 immunoprecipitated from SK-BR-3 cells was in-gel digested with trypsin, chymotrypsin, Asp-N, or trypsin plus Asp-N in triplicate. Data-dependent acquisition with an AB SCIEX TripleTOF 5600 and MS1 Filtering data processing was used to assess peptide and PTM coverage as well as the reproducibility of enzyme digestion. Data-independent acquisition (SWATH) was also performed for MS2 quantitation. MS1 Filtering and SWATH MS2 allow quantitation of all detected analytes after acquisition, enabling the use of multiple proteases for quantitative assessment of target proteins. Combining high resolution proteomics with multiprotease digestion enabled quantitative mapping of ErbB2 with excellent reproducibility, improved amino acid sequence and PTM coverage, and decreased assay development time compared to typical SRM assays. These results demonstrate that high resolution quantitative proteomic approaches are an effective tool for targeted biomarker quantitation.
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Montaño-Gutierrez, Luis F., Shinya Ohta, Georg Kustatscher, William C. Earnshaw, and Juri Rappsilber. "Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data." Molecular Biology of the Cell 28, no. 5 (March 2017): 673–80. http://dx.doi.org/10.1091/mbc.e16-06-0370.
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Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex’s signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online.
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Kraus, Milena, Mariet Mathew Stephen, and Matthieu-P. Schapranow. "Eatomics: Shiny Exploration of Quantitative Proteomics Data." Journal of Proteome Research 20, no. 1 (September 21, 2020): 1070–78. http://dx.doi.org/10.1021/acs.jproteome.0c00398.
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Chia-Yu Yen, S. M. Helmike, K. J. Cios, M. B. Perryman, and M. W. Duncan. "Quantitative analysis of proteomics using data mining." IEEE Engineering in Medicine and Biology Magazine 24, no. 3 (May 2005): 67–72. http://dx.doi.org/10.1109/memb.2005.1436462.
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Handler, David C., Dana Pascovici, Mehdi Mirzaei, Vivek Gupta, Ghasem Hosseini Salekdeh, and Paul A. Haynes. "The Art of Validating Quantitative Proteomics Data." PROTEOMICS 18, no. 23 (November 25, 2018): 1800222. http://dx.doi.org/10.1002/pmic.201800222.
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Santos, Marlon D. M., Amanda Caroline Camillo-Andrade, Louise U. Kurt, Milan A. Clasen, Eduardo Lyra, Fabio C. Gozzo, Michel Batista, et al. "Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition." Journal of Proteomics 222 (June 2020): 103803. http://dx.doi.org/10.1016/j.jprot.2020.103803.
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Peng, Gang, Rashaun Wilson, Yishuo Tang, TuKiet T. Lam, Angus C. Nairn, Kenneth Williams, and Hongyu Zhao. "ProteomicsBrowser: MS/proteomics data visualization and investigation." Bioinformatics 35, no. 13 (November 21, 2018): 2313–14. http://dx.doi.org/10.1093/bioinformatics/bty958.
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Abstract Summary Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. Availability and implementation ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. Supplementary information Supplementary data are available at Bioinformatics online.
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Röst, Hannes L., Lars Malmström, and Ruedi Aebersold. "Reproducible quantitative proteotype data matrices for systems biology." Molecular Biology of the Cell 26, no. 22 (November 5, 2015): 3926–31. http://dx.doi.org/10.1091/mbc.e15-07-0507.
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Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.
Dissertations / Theses on the topic "Quantitative proteomics data"
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Ahmad, Yasmeen. "Management, visualisation & mining of quantitative proteomics data." Thesis, University of Dundee, 2012. https://discovery.dundee.ac.uk/en/studentTheses/6ed071fc-e43b-410c-898d-50529dc298ce.
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Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (http://www.peptracker.com). This software provides a framework for the systematic detection and analysis of proteins that can be correlated with biological properties to expand the functional annotation of the genome. The tools created in this study aim to place analysis capabilities back in the hands of biologists, who are expert in evaluating their data. Another major advantage of the PepTracker suite is the implementation of a data warehouse, which manages and collates highly annotated experimental data from numerous experiments carried out by many researchers. This repository captures the collective experience of a laboratory, which can be accessed via user-friendly interfaces. Rather than viewing datasets as isolated components, this thesis explores the potential that can be gained from collating datasets in a “super-experiment” ideology, leading to formation of broad ranging questions and promoting biology driven lines of questioning. This has been uniquely implemented by integrating tools and techniques from the field of Business Intelligence with Life Sciences and successfully shown to aid in the analysis of proteomic interaction experiments. Having conquered a means of documenting a static proteomics snapshot of cells, the proteomics field is progressing towards understanding the extremely complex nature of cell dynamics. PepTracker facilitates this by providing the means to gather and analyse many protein properties to generate new biological insight, as demonstrated by the identification of novel protein isoforms.
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Lee, Wooram. "Protein Set for Normalization of Quantitative Mass Spectrometry Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54554.
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Mass spectrometry has been recognized as a prominent analytical technique for peptide and protein identification and quantitation. With the advent of soft ionization methods, such as electrospray ionization and matrix assisted laser desorption/ionization, mass spectrometry has opened a new era for protein and proteome analysis. Due to its high-throughput and high-resolution character, along with the development of powerful data analysis software tools, mass spectrometry has become the most popular method for quantitative proteomics.
Stable isotope labeling and label-free quantitation methods are widely used in quantitative mass spectrometry experiments. Proteins with stable expression level and key roles in basic cellular functions such as actin, tubulin and glyceraldehyde-3-phosphate dehydrogenase, are frequently utilized as internal controls in biological experiments. However, recent studies have shown that the expression level of such commonly used housekeeping proteins is dependent on cell type, cell cycle or disease status, and that it can change as a result of a biochemical stimulation. Such phenomena can, therefore, substantially compromise the use of these proteins for data validation.
In this work, we propose a novel set of proteins for quantitative mass spectrometry that can be used either for data normalization or validation purposes. The protein set was generated from cell cycle experiments performed with MCF-7, an estrogen receptor positive breast cancer cell line, and MCF-10A, a non-tumorigenic immortalized breast cell line. The protein set was selected from a list of 3700 proteins identified in the different cellular sub-fractions and cell cycle stages of MCF-7/MCF-10A cells, based on the stability of spectral count data (CV<30 %) generated with an LTQ ion trap mass spectrometer. A total of 34 proteins qualified as endogenous standards for the nuclear, and 75 for the cytoplasmic cell fractions, respectively. The validation of these proteins was performed with a complementary, Her2+, SKBR-3 cell line. Based on the outcome of these experiments, it is anticipated that the proposed protein set will find applicability for data normalization/validation in a broader range of mechanistic biological studies that involve the use of cell lines.Master of Science
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McQueen, Peter. "Alternative strategies for proteomic analysis and relative protein quantitation." Wiley-VCH, 2015. http://hdl.handle.net/1993/30850.
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The main approach to studying the proteome is a technique called data dependent acquisition (DDA). In DDA, peptides are analyzed by mass spectrometry to determine the protein composition of a biological isolate. However, DDA is limited in its ability to analyze the proteome, in that it only selects the most abundant ions for analysis, and different protein identifications can result even if the same sample is analyzed multiple times in succession. Data independent acquisition (DIA) is a newly developed method that should be able to solve these limitations and improve our ability to analyze the proteome. We used an implementation of DIA (SWATH) to perform relative protein quantitation in the model bacterial system, Clostridium stercorarium, using two different carbohydrate sources, and found that it was able to provide precise quantitation of proteins and was overall more consistent in its ability to identify components of the proteome than DDA.
Relative quantitation of proteins is an important method that can determine which proteins are important to a biochemical process of interest. How we determine which proteins are differentially regulated between different conditions is an important question in proteomic analysis. We developed a new approach to analyzing differential protein expression using variation between biological replicates to determine which proteins are being differentially regulated between two conditions. This analysis showed that a large proportion of proteins identified by quantitative proteomic analysis can be differentially regulated and that these proteins are in fact related to biological processes.
Analyzing changes in protein expression is a useful tool that can pinpoint many key processes in biological systems. However, these techniques fail to take into account that enzyme activity is regulated by other factors than controlling their level of expression. Activity based protein profiling (ABPP) is a method that can determine the activity state of an enzyme in whole cell proteomes. We found that enzyme activity can change in response to a number of different conditions and that these changes do not always correspond with compositional changes. Mass spectrometry techniques were also used to identify serine hydrolases and characterize their expression in this organism.February 2016
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May, Patrick, Jan-Ole Christian, Stefan Kempa, and Dirk Walther. "ChlamyCyc : an integrative systems biology database and web-portal for Chlamydomonas reinhardtii." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2010/4494/.
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Background:
The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic
model organism for the study of photosynthesis and plant growth. In the era of modern highthroughput technologies there is an imperative need to integrate large-scale data sets from highthroughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism.
Results:
In the framework of the German Systems Biology initiative GoFORSYS, a pathway
database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification.
Conclusion:
ChlamyCyc provides a curated and integrated systems biology repository that will
enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.
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Chion, Marie. "Développement de nouvelles méthodologies statistiques pour l'analyse de données de protéomique quantitative." Thesis, Strasbourg, 2021. http://www.theses.fr/2021STRAD025.
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L’analyse protéomique consiste à étudier l’ensemble des protéines exprimées par un système biologique donné, à un moment donné et dans des conditions données. Les récents progrès technologiques en spectrométrie de masse et en chromatographie liquide permettent d’envisager aujourd’hui des études protéomiques à large échelle et à haut débit. Ce travail de thèse porte sur le développement de méthodologies statistiques pour l’analyse des données de protéomique quantitative et présente ainsi trois principales contributions. La première partie propose d’utiliser des modèles de régression par spline monotone pour estimer les quantités de tous les peptides détectés dans un échantillon grâce à l'utilisation de standards internes marqués pour un sous-ensemble de peptides ciblés. La deuxième partie présente une stratégie de prise en compte de l’incertitude induite par le processus d’imputation multiple dans l’analyse différentielle, également implémentée dans le package R mi4p. Enfin, la troisième partie propose un cadre bayésien pour l’analyse différentielle, permettant notamment de tenir compte des corrélations entre les intensités des peptidesProteomic analysis consists of studying all the proteins expressed by a given biological system, at a given time and under given conditions. Recent technological advances in mass spectrometry and liquid chromatography make it possible to envisage large-scale and high-throughput proteomic studies.This thesis work focuses on developing statistical methodologies for the analysis of quantitative proteomics data and thus presents three main contributions. The first part proposes to use monotone spline regression models to estimate the amounts of all peptides detected in a sample using internal standards labelled for a subset of targeted peptides. The second part presents a strategy to account for the uncertainty induced by the multiple imputation process in the differential analysis, also implemented in the mi4p R package. Finally, the third part proposes a Bayesian framework for differential analysis, making it notably possible to consider the correlations between the intensities of peptides
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Husson, Gauthier. "Development of host cell protein impurities quantification methods by mass spectrometry to control the quality of biopharmaceuticals." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAF066/document.
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Les récents progrès instrumentaux en spectrométrie de masse, notamment en terme de- rapidité de balayage et de résolution, ont permis l'émergence de l'approche « data independent acquisition» (DIA). Cette approche promet de combiner les points forts des approches « shotgun » et ciblées,mais aujourd'hui l'analyse des données DIA reste compliquée. L'objectif de cette thèse a été de développer des méthodes innovantes de spectrométrie de masse, et en particulier d'améliorer l'analyse des données DIA. De plus, nous avons développé une approche originale Top 3-ID-DIA, permettant à la fois un profilage complet des protéines de la cellule hôte (HCP) ainsi qu'une quantification absolue d'HCP clés dans les échantillons d'anticorps monoclonaux (mAb), au sein d'une même analyse.Cette méthode est prête à être implémentée en industrie, et pourrait fournir un support en temps réel aux développements du procédé de production de mAb, ainsi que pour évaluer la pureté des biomédicamentsRecent instrumental developments in mass spectrometry, notably in terms of scan speed and resolution, allowed the emergence of “data independent acquisition” (DIA) approach. This approach promises to combine the strengths of both shotgun and targeted proteomics, but today DIA data analysis remains challenging. The objective of my PhD was to develop innovative mass spectrometry approaches, and in particular to improve DIA data analysis. Moreover, we developed an original Top 3-ID-DIA approach, allowing both a global profiling of host cell proteins (HCP) and an absolute quantification of key HCP in monoclonal antibodies samples, within a single analysis. This method is ready to be transferred to industry, and could provide a real time support for mAb manufacturing process development, as well as for product purity assessment
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Denecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.
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Plusieurs évolutions sont constatées dans la recherche en biologie. Tout d’abord, les études menées reposent souvent sur des approches expérimentales quantitatives. L’analyse et l’interprétation des résultats requièrent l’utilisation de l’informatique et des statistiques. Également, en complément des études centrées sur des objets biologiques isolés, les technologies expérimentales haut débit permettent l’étude des systèmes (caractérisation des composants du système ainsi que des interactions entre ces composants). De très grandes quantités de données sont disponibles dans les bases de données publiques, librement réutilisables pour de nouvelles problématiques. Enfin, les données utiles pour les recherches en biologie sont très hétérogènes (données numériques, de textes, images, séquences biologiques, etc.) et conservées sur des supports d’information également très hétérogènes (papiers ou numériques). Ainsi « l’analyse de données » s’est petit à petit imposée comme une problématique de recherche à part entière et en seulement une dizaine d’années, le domaine de la « Bioinformatique » s’est en conséquence totalement réinventé. Disposer d’une grande quantité de données pour répondre à un questionnement biologique n’est souvent pas le défi principal. La vraie difficulté est la capacité des chercheurs à convertir les données en information, puis en connaissance. Dans ce contexte, plusieurs problématiques de recherche en biologie ont été abordées lors de cette thèse. La première concerne l’étude de l’homéostasie du fer chez la levure pathogène Candida glabrata. La seconde concerne l’étude systématique des modifications post-traductionnelles des protéines chez la levure pathogène Candida albicans. Pour ces deux projets, des données « omiques » ont été exploitées : transcriptomiques et protéomiques. Des outils bioinformatiques et des outils d’analyses ont été implémentés en parallèle conduisant à l’émergence de nouvelles hypothèses de recherche en biologie. Une attention particulière et constante a aussi été portée sur les problématiques de reproductibilité et de partage des résultats avec la communauté scientifiqueBiological research is changing. First, studies are often based on quantitative experimental approaches. The analysis and the interpretation of the obtained results thus need computer science and statistics. Also, together with studies focused on isolated biological objects, high throughput experimental technologies allow to capture the functioning of biological systems (identification of components as well as the interactions between them). Very large amounts of data are also available in public databases, freely reusable to solve new open questions. Finally, the data in biological research are heterogeneous (digital data, texts, images, biological sequences, etc.) and stored on multiple supports (paper or digital). Thus, "data analysis" has gradually emerged as a key research issue, and in only ten years, the field of "Bioinformatics" has been significantly changed. Having a large amount of data to answer a biological question is often not the main challenge. The real challenge is the ability of researchers to convert the data into information and then into knowledge. In this context, several biological research projects were addressed in this thesis. The first concerns the study of iron homeostasis in the pathogenic yeast Candida glabrata. The second concerns the systematic investigation of post-translational modifications of proteins in the pathogenic yeast Candida albicans. In these two projects, omics data were used: transcriptomics and proteomics. Appropriate bioinformatics and analysis tools were developed, leading to the emergence of new research hypotheses. Particular and constant attention has also been paid to the question of data reproducibility and sharing of results with the scientific community
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Liu, X., L. Hu, G. Ge, B. Yang, J. Ning, S. Sun, L. Yang, Klaus Pors, and J. Gu. "Quantitative analysis of cytochrome P450 isoforms in human liver microsomes by the combination of proteomics and chemical probe-based assay." 2014. http://hdl.handle.net/10454/10502.
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NoCytochrome P450 (CYP) is one of the most important drug-metabolizing enzyme families, which participates in the biotransformation of many endogenous and exogenous compounds. Quantitative analysis of CYP expression levels is important when studying the efficacy of new drug molecules and assessing drug-drug interactions in drug development. At present, chemical probe-based assay is the most widely used approach for the evaluation of CYP activity although there are cross-reactions between the isoforms with high sequence homologies. Therefore, quantification of each isozyme is highly desired in regard to meeting the ever-increasing requirements for carrying out pharmacokinetics and personalized medicine in the academic, pharmaceutical, and clinical setting. Herein, an absolute quantification method was employed for the analysis of the seven isoforms CYP1A2, 2B6, 3A4, 3A5, 2C9, 2C19, and 2E1 using a proteome-derived approach in combination with stable isotope dilution assay. The average absolute amount measured from twelve human liver microsomes samples were 39.3, 4.3, 54.0, 4.6, 10.3, 3.0, and 9.3 (pmol/mg protein) for 1A2, 2B6, 3A4, 3A5, 2C9, 2C19, and 2E1, respectively. Importantly, the expression level of CYP3A4 showed high correlation (r = 0.943, p < 0.0001) with the functional activity, which was measured using bufalin-a highly selective chemical probe we have developed. The combination of MRM identification and analysis of the functional activity, as in the case of CYP3A4, provides a protocol which can be extended to other functional enzyme studies with wide application in pharmaceutical research.
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Lai, En-Yu, and 賴恩語. "Functional Analysis for Quantitative Proteomic Data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/scxa8y.
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博士國立陽明大學
生物醫學資訊研究所
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Approaches to identify significant pathways from high-throughput quantitative data have been well-established in recent years. Still, the analysis of proteomic data stays difficult because of its incomplete nature. Compared to gene expression data, proteomic data usually have smaller sample size, fewer identified entities, and the results are sensitive to experimental conditions and instruments. When applying pathway analysis, limited sample size leads to the practice of using a competitive null as a common approach; which fundamentally implies proteins as independent units. The independent assumption ignores the associations among proteins with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among proteins and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. Fewer identified entities also become an important issue when locating responsive subpathways. Current approaches usually use a lot of network properties to locate the subpathways on a PPI network, but the sparse data restrain their ability to connect the proteins. Using a PPI network as the reference of biological interactions may over simplify the problem as well. Most of PPI networks do not provide the information of regulation types, eliminate metabolites and other small chemicals, and ignore the fact that the subunits of a protein complex may not be functional alone. Another common issue rises from the experimental design. Proteomic studies usually focus on specific proteome. Proteins do not belong the specific proteome are inaccessible under current experimental condition. In this thesis, we introduce a systematic analyzing scheme for quantitative proteomic data. For any experiment under a comparative condition, we perform a pathway analysis using a multivariate T2-test under a self-contained null. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. For time-course experiments, we further apply a genetic algorithm to locate the responsive subpathways hidden inside the significant pathways tested by the proposed T2-statistic. The genetic algorithm is designed to detect subpathways of higher expression ratio with coherent relationship. We also take protein accessibility and the composition of protein complex into consideration. The reference pathways are provided by KEGG; and the proteome accessibility by UniProt. The performance of the proposed analyzing scheme is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other statistics of pathway analysis, the T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We also compare the genetic algorithm with jActiveModules and BioNet. Generally, the subpathways reported by other tools are highly-connected networks, hence the performance shrinks as long as the data are sparse. On the other hand, the proposed algorithm is more tolerated to missing or inaccessible proteins, so we are able to provide candidate subpathways even when the data include fewer identified entities. We implemented the proposed T2-statistic and genetic algorithm into an R package T2GA, which is available at https://github.com/roqe/T2GA.
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Wang, Xuan. "Statistical Methods for the Analysis of Mass Spectrometry-based Proteomics Data." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10777.
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Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification.
We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet.
Several alignment algorithms have been developed using different warping functions. However, all the existing alignment approaches suffer from a useful metric for scoring an alignment between two data sets and hence lack a quantitative score for how good an alignment is. Our alignment approach uses "Anchor points" found to align all the individual scan in the target sample and provides a framework to quantify the alignment, that is, assigning a p-value to a set of aligned LC-MS runs to assess the correctness of alignment. After alignment using our algorithm, the p-values from Wilcoxon signed-rank test on elution (retention) time, M/Z, peak area successfully turn into non-significant values.
Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical mass spectrometry-based proteomics data sets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of "presence / absence", we enable the selection of proteins not typically amendable to quantitative analysis; e.g., "one-state" proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence / absence analysis of a given data set in a principled way, resulting in a single list of selected proteins with a single associated FDR.
Book chapters on the topic "Quantitative proteomics data"
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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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Levin, Yishai. "CHAPTER 8. Label-free Quantification of Proteins Using Data-Independent Acquisition." In Quantitative Proteomics, 175–84. Cambridge: Royal Society of Chemistry, 2014. http://dx.doi.org/10.1039/9781782626985-00175.
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Christoforou, Andy, Claire Mulvey, Lisa M. Breckels, Laurent Gatto, and Kathryn S. Lilley. "CHAPTER 9. Spatial Proteomics: Practical Considerations for Data Acquisition and Analysis in Protein Subcellular Localisation Studies." In Quantitative Proteomics, 185–210. Cambridge: Royal Society of Chemistry, 2014. http://dx.doi.org/10.1039/9781782626985-00185.
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Meyer, Jesse G. "Qualitative and Quantitative Data Analysis from Data-Dependent." In Shotgun Proteomics, 297–308. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1178-4_19.
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Pietilä, Sami, Tomi Suomi, Juhani Aakko, and Laura L. Elo. "A Data Analysis Protocol for Quantitative Data-Independent Acquisition Proteomics." In Functional Proteomics, 455–65. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8814-3_27.
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Distler, Ute, Jörg Kuharev, Hansjörg Schild, and Stefan Tenzer. "Data-independent acquisition strategies for quantitative proteomics." In Farm animal proteomics 2013, 51–54. Wageningen: Wageningen Academic Publishers, 2013. http://dx.doi.org/10.3920/978-90-8686-776-9_16.
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Breitwieser, Florian Paul, and Jacques Colinge. "Analysis of Labeled Quantitative Mass Spectrometry Proteomics Data." In Computational Medicine, 79–91. Vienna: Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0947-2_5.
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Pham, Thang V., and Connie R. Jimenez. "Quantitative Analysis of Mass Spectrometry-Based Proteomics Data." In Neuromethods, 129–42. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9662-9_12.
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Sonnett, Matthew, Meera Gupta, Thao Nguyen, and Martin Wühr. "Quantitative Proteomics for Xenopus Embryos II, Data Analysis." In Methods in Molecular Biology, 195–215. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8784-9_14.
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Griss, Johannes, and Veit Schwämmle. "Analysis of Label-Based Quantitative Proteomics Data Using." In Methods in Molecular Biology, 61–73. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1641-3_4.
Full textConference papers on the topic "Quantitative proteomics data"
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Tekwe, Carmen D., Alan R. Dabney, and Raymond J. Carroll. "Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169453.
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Soon-Shiong, P., S. Rabizadeh, S. Benz, F. Cecchi, T. Hembrough, E. Mahen, K. Burton, et al. "Abstract P6-05-08: Integrating whole exome sequencing data with RNAseq and quantitative proteomics to better inform clinical treatment decisions in patients with metastatic triple negative breast cancer." In Abstracts: Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium; December 8-12, 2015; San Antonio, TX. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.sabcs15-p6-05-08.
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Benz, SC, S. Rabizadeh, F. Cecchi, MW Beckman, SY Brucker, A. Hartmann, J. Golovato, et al. "Abstract P6-04-14: Integrating whole genome sequencing data with RNAseq, pathway analysis, and quantitative proteomics to determine prognosis after standard adjuvant treatment with trastuzumab and chemotherapy in primary breast cancer patients." In Abstracts: Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium; December 8-12, 2015; San Antonio, TX. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.sabcs15-p6-04-14.
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Jiang, Biaobin, David F. Gleich, and Michael Gribskov. "Differential flux balance analysis of quantitative proteomic data on protein interaction networks." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418343.
Full textReports on the topic "Quantitative proteomics data"
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Chen, Xiaole, Peng Wang, Yunquan Luo, Yi-Yu Lu, Wenjun Zhou, Mengdie Yang, Jian Chen, Zhi-Qiang Meng, and Shi-Bing Su. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, September 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.
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Objective: The aim of this study was to assess the therapeutic effects of Jianpi Liqi decoction (JPLQD) in hepatocellular carcinoma (HCC) and explore its underlying mechanisms. Methods: The characteristics and outcomes of HCC patients with intermediate stage B who underwent sequential conventional transcatheter arterial chemoembolization (cTACE) and radiofrequency ablation (RFA) only or in conjunction with JPLQD were analysed retrospectively. The plasma proteins were screened using label-free quantitative proteomics analysis. The effective mechanisms of JPLQD were predicted through network pharmacology approach and partially verified by ELISA. Results: Clinical research demonstrated that the Karnofsky Performance Status (KPS), traditional Chinese medicine (TCM) syndrome scores, neutropenia and bilirubin, median progression-free survival (PFS), and median overall survival (OS) in HCC patients treated with JPLQD were superior to those in patients not treated with JPLQD (all P<0.05). The analysis of network pharmacology, combined with proteomics, suggested that 52 compounds targeted 80 potential targets, which were involved in the regulation of multiple signaling pathways, especially affecting the apoptosis-related pathways including TNF, p53, PI3K-AKT, and MAPK. Plasma IGFBP3 and CA2 were significantly up-regulated in HCC patients with sequential cTACE and RFA therapy treated with JPLQD than those in patients not treated with JPLQD (P<0.001). The AUC of the IGFBP3 and CA2 panel, estimated using ROC analysis for JPLQD efficacy evaluation, was 0.867. Conclusion: These data suggested that JPLQD improves the quality of life, prolongs the overall survival, protects liver function in HCC patients, and exhibits an anticancer activity against HCC. IGFBP3 and CA2 panels may be potential therapeutic targets and indicators in the efficacy evaluation for JPLQD treatment, and the effective mechanisms involved in the regulation of multiple signaling pathways, possibly affected the regulation of apoptosis.
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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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Epel, Bernard, and Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, November 2005. http://dx.doi.org/10.32747/2005.7695874.bard.
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To cause disease, plant viruses must replicate and spread locally and systemically within the host. Cell-to-cell virus spread is mediated by virus-encoded movement proteins (MPs), which modify the structure and function of plasmodesmata (Pd), trans-wall co-axial membranous tunnels that interconnect the cytoplasm of neighboring cells. Tobacco mosaic virus (TMV) employ a single MP for cell- cell spread and for which CP is not required. The PIs, Beachy (USA) and Epel (Israel) and co-workers, developed new tools and approaches for study of the mechanism of spread of TMV that lead to a partial identification and molecular characterization of the cellular machinery involved in the trafficking process. Original research objectives: Based on our data and those of others, we proposed a working model of plant viral spread. Our model stated that MPᵀᴹⱽ, an integral ER membrane protein with its C-terminus exposed to the cytoplasm (Reichel and Beachy, 1998), alters the Pd SEL, causes the Pd cytoplasmic annulus to dilate (Wolf et al., 1989), allowing ER to glide through Pd and that this gliding is cytoskeleton mediated. The model claimed that in absence of MP, the ER in Pd (the desmotubule) is stationary, i.e. does not move through the Pd. Based on this model we designed a series of experiments to test the following questions: -Does MP potentiate ER movement through the Pd? - In the presence of MP, is there communication between adjacent cells via ER lumen? -Does MP potentiate the movement of cytoskeletal elements cell to cell? -Is MP required for cell-to-cell movement of ER membranes between cells in sink tissue? -Is the binding in situ of MP to RNA specific to vRNA sequences or is it nonspecific as measured in vitro? And if specific: -What sequences of RNA are involved in binding to MP? And finally, what host proteins are associated with MP during intracellular targeting to various subcellular targets and what if any post-translational modifications occur to MP, other than phosphorylation (Kawakami et al., 1999)? Major conclusions, solutions and achievements. A new quantitative tool was developed to measure the "coefficient of conductivity" of Pd to cytoplasmic soluble proteins. Employing this tool, we measured changes in Pd conductivity in epidermal cells of sink and source leaves of wild-type and transgenic Nicotiana benthamiana (N. benthamiana) plants expressing MPᵀᴹⱽ incubated both in dark and light and at 16 and 25 ᵒC (Liarzi and Epel, 2005 (appendix 1). To test our model we measured the effect of the presence of MP on cell-to-cell spread of a cytoplasmic fluorescent probe, of two ER intrinsic membrane protein-probes and two ER lumen protein-probes fused to GFP. The effect of a mutant virus that is incapable of cell-to-cell spread on the spread of these probes was also determined. Our data shows that MP reduces SEL for cytoplasmic molecules, dilates the desmotubule allowing cell-cell diffusion of proteins via the desmotubule lumen and reduces the rate of spread of the ER membrane probes. Replicase was shown to enhance cell-cell spread. The data are not in support of the proposed model and have led us to propose a new model for virus cell-cell spread: this model proposes that MP, an integral ER membrane protein, forms a MP:vRNAER complex and that this ER-membrane complex diffuses in the lipid milieu of the ER into the desmotubule (the ER within the Pd), and spreads cell to cell by simple diffusion in the ER/desmotubule membrane; the driving force for spread is the chemical potential gradient between an infected cell and contingent non-infected neighbors. Our data also suggests that the virus replicase has a function in altering the Pd conductivity. Transgenic plant lines that express the MP gene of the Cg tobamovirus fused to YFP under the control the ecdysone receptor and methoxyfenocide ligand were generated by the Beachy group and the expression pattern and the timing and targeting patterns were determined. A vector expressing this MPs was also developed for use by the Epel lab . The transgenic lines are being used to identify and isolate host genes that are required for cell-to-cell movement of TMV/tobamoviruses. This line is now being grown and to be employed in proteomic studies which will commence November 2005. T-DNA insertion mutagenesis is being developed to identify and isolate host genes required for cell-to-cell movement of TMV.