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Thematische Bibliographien / DNA microarrays

Auswahl der wissenschaftlichen Literatur zum Thema „DNA microarrays“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 22. Februar 2023

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Inhaltsverzeichnis

Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte
Berichte der Organisationen

Zeitschriftenartikel zum Thema "DNA microarrays":

1

Weitzman, Jonathan B. „DNA/DNA microarrays“. Genome Biology 2 (2001): spotlight—20010813–03. http://dx.doi.org/10.1186/gb-spotlight-20010813-03.

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2

Hofman, Paul. „DNA Microarrays“. Nephron Physiology 99, Nr. 3 (24.02.2005): p85—p89. http://dx.doi.org/10.1159/000083764.

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3

Cook, Stuart A., und Anthony Rosenzweig. „DNA Microarrays“. Circulation Research 91, Nr. 7 (04.10.2002): 559–64. http://dx.doi.org/10.1161/01.res.0000036019.55901.62.

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4

Niemeyer, Christof M., und Dietmar Blohm. „DNA Microarrays“. Angewandte Chemie International Edition 38, Nr. 19 (04.10.1999): 2865–69. http://dx.doi.org/10.1002/(sici)1521-3773(19991004)38:19<2865::aid-anie2865>3.0.co;2-f.

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5

Biesen, R., und T. Häupl. „DNA-Microarrays“. Zeitschrift für Rheumatologie 70, Nr. 9 (30.09.2011): 803–8. http://dx.doi.org/10.1007/s00393-011-0869-4.

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6

Whipple, Mark Eliot, und Winston Patrick Kuo. „DNA Microarrays in Otolaryngology-Head and Neck Surgery“. Otolaryngology–Head and Neck Surgery 127, Nr. 3 (September 2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.

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OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarray technologies are having a substantial impact on biomedical research, including many areas relevant to otolaryngology-head and neck surgery. CONCLUSIONS: DNA microarrays allow for the simultaneous investigationof thousands of individual genes in a single experiment. In the coming years, the application of these technologies to clinical medicine should allow for unprecedented methods ofdiagnosis and treatment. SIGNIFICANCE: These highly parallel experimental techniques promise to revolutionize gene discovery, disease characterization, and drug development.

7

Peiffer, Daniel, Ken Cho und Yongchol Shin. „Xenopus DNA Microarrays“. Current Genomics 4, Nr. 8 (01.11.2003): 665–72. http://dx.doi.org/10.2174/1389202033490097.

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8

Schofield, W. C. E., J. McGettrick, T. J. Bradley, J. P. S. Badyal und S. Przyborski. „Rewritable DNA Microarrays“. Journal of the American Chemical Society 128, Nr. 7 (Februar 2006): 2280–85. http://dx.doi.org/10.1021/ja056367r.

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9

HENRY, CELIA M. „STANDARDIZING DNA MICROARRAYS“. Chemical & Engineering News 82, Nr. 31 (02.08.2004): 36–39. http://dx.doi.org/10.1021/cen-v082n031.p036.

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Paredes, Carlos J., Ryan S. Senger, Iwona S. Spath, Jacob R. Borden, Ryan Sillers und Eleftherios T. Papoutsakis. „A General Framework for Designing and Validating Oligomer-Based DNA Microarrays and Its Application to Clostridium acetobutylicum“. Applied and Environmental Microbiology 73, Nr. 14 (25.05.2007): 4631–38. http://dx.doi.org/10.1128/aem.00144-07.

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ABSTRACT While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.

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Dissertationen zum Thema "DNA microarrays":

1

Stephens, Nathan W. „A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /“. Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.

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2

Brunner, Thomas. „Designing oligonucleotides for DNA microarrays /“. Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, 2003. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=116.

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3

Smith, Kaleigh. „Towards quality control in DNA microarrays“. Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=79129.

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We present a framework for detecting degenerate probes in a DNA microarray that may add to measurement error in hybridization experiments. We consider four types of behaviour: secondary structure formation, self-dimerization, cross-hybridization and dimerization. The framework uses a well-established model of nucleic acid sequence hybridization and a novel method for the detection of patterns in hybridization experiment data. Our primary result is the identification of unique patterns in hybridization experiment data that are correlated with each type of degenerate probe behaviour. The framework also contains a machine learning technique to learn from the hybridization experiment data. We implement the components of the framework and evaluate the ability of the framework to detect degenerate probes in the Affymetrix HuGeneFL GeneChip.

4

Arrais, Joel Perdiz. „Sistemas de informação para DNA microarrays“. Doctoral thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/2232.

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Doutoramento em Engenharia Informática
O projecto de sequenciação do genoma humano veio abrir caminho para o surgimento de novas áreas transdisciplinares de investigação, como a biologia computacional, a bioinformática e a bioestatística. Um dos resultados emergentes desde advento foi a tecnologia de DNA microarrays, que permite o estudo do perfil da expressão de milhares de genes, quando sujeitos a perturbações externas. Apesar de ser uma tecnologia relativamente consolidada, continua a apresentar um conjunto vasto de desafios, nomeadamente do ponto de vista computacional e dos sistemas de informação. São exemplos a optimização dos procedimentos de tratamento de dados bem como o desenvolvimento de metodologias de interpretação semi-automática dos resultados. O principal objectivo deste trabalho consistiu em explorar novas soluções técnicas para agilizar os procedimentos de armazenamento, partilha e análise de dados de experiências de microarrays. Com esta finalidade, realizou-se uma análise de requisitos associados às principais etapas da execução de uma experiência, tendo sido identificados os principais défices, propostas estratégias de melhoramento e apresentadas novas soluções. Ao nível da gestão de dados laboratoriais, é proposto um LIMS (Laboratory Information Management System) que possibilita a gestão de todos os dados gerados e dos procedimentos realizados. Este sistema integra ainda uma solução que permite a partilha de experiências, de forma a promover a participação colaborativa de vários investigadores num mesmo projecto, mesmo usando LIMS distintos. No contexto da análise de dados, é apresentado um modelo que facilita a integração de algoritmos de processamento e de análise de experiências no sistema desenvolvido. Por fim, é proposta uma solução para facilitar a interpretação biológica de um conjunto de genes diferencialmente expressos, através de ferramentas que integram informação existente em diversas bases de dados biomédicas.
The sequencing of the human genome paved the way for the emergence of new transdisciplinary research areas, such as computational biology, bioinformatics and biostatistics. One example of such is the advent of DNA microarray technology, which allows the study of the expression of thousands of genes when subjected to an external disturbance. Despite being a well-established technology, it continues to present a wide range of challenges, particularly in terms of computing and information systems. Examples include the optimization of procedures for processing data as well as the development of methodologies for semi-automated interpretation of results. The main objective of this study was to explore new technical solutions to streamline the procedures for storing, sharing and analyzing the data from microarray experiments. To this end, it was performed an analysis of the key steps from the experiment, having been identified the major deficits, proposed strategies for improving and presented new solutions. Regarding the management of laboratory data we propose a LIMS (Laboratory Information Management System) that allows the storage of all data generated and procedures performed in the laboratory. This system also includes a solution that enables the sharing of experiments in order to promote collaborative participation of several researchers in the same project, even using different LIMS. In the context of data analysis, it is presented a model that allows the simplified integration of processing and analysis algorithms in the developed system. Finally, it is proposed a solution to facilitate the biological interpretation of a set of differentially expressed genes, using tools that integrate information from several public biomedical databases.

5

Harness, Denise. „A Comparison of Unsupervised Methods for DNA Microarray Leukemia Data“. Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/106.

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Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which associations between genes are most important to predicting Leukemia diagnosis. This technique reduces the number of genes to a fraction of the original. In this approach, partial correlations are then extended into a spectral clustering approach. In particular, a variety of different Laplacian matrices are generated from the network of connections between features, and each implies a graphical network model of gene interconnectivity. Various edge and vertex weighted Laplacians are considered and compared against each other in a probabilistic graphical modeling approach. The resulting multivariate Gaussian distributed clusters are subsequently analyzed to determine which genes are activated in a patient with Leukemia. Finally, the results of this are compared against other feature engineering approaches to assess its accuracy on the Leukemia data set. The initial results show the partial correlation approach of feature selection predicts the diagnosis of a Leukemia patient with almost the same accuracy as using a machine learning algorithm on the full set of genes. More calculations of the precision matrix are needed to ensure the set of most important genes is correct. Additionally more machine learning algorithms will be implemented using the full and reduced data sets to further validate the current prediction accuracy of the partial correlation method.

6

Chow, Brian 1978. „Photoelectromechanical synthesis of low-cost DNA microarrays“. Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/42405.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.
Includes bibliographical references.
Recent advances in de novo gene synthesis, library construction, and genomic selection for target sequencing using DNA from custom microarrays have demonstrated that microarrays can effectively be used as the world's cheapest sources of complex oligonucleotide pools. Unfortunately, commercial custom microarrays are expensive and not easily accessible to academic researchers, and technical challenges still exist for dealing with the small amount of DNA synthesized on a chip. Genomic research would certainly benefit from the creation of cheaper custom microarrays with larger oligonucleotide concentrations per spot. This thesis presents the development of a novel DNA microarray synthesis platform based on semiconductor photoelectrochemistry (PEC) designed with these needs in mind. An amorphous silicon photoconductor is activated by an optical projection system to create "virtual electrodes" that electrochemically generate protons in a site-selective manner, thereby cleaving acid-labile dimethoxytrityl protecting groups with the spatial selectivity that is required for in-situ DNA synthesis. This platform has the potential to be particularly low-cost since it employs standard phosphoramidite reagents, visible wavelength optics, and a cheaply microfabricated and reusable substrate. By incorporating a porous thin-film glass that dramatically increases the DNA quantity produced by over an order of magnitude per chip, this platform may also simplify the handling of DNA cleaved from chip and drive down the cost per base synthesized. The hybridization detection of single-base errors was successfully demonstrated on PEC synthesized microarrays. This thesis also reports a suite of new surface chemistries and high-resolution techniques for patterning biological molecules.
by Brian Yichiun Chow.
Ph.D.

7

Horschinek, Andreas. „DNA-Microarrays zur therapiebegleitenden Prognose bei Brustkrebs“. [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-27654.

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8

Xue, Mei. „Array based integrated DNA identification system for genetic chip application /“. View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20XUE.

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9

Peeters, Justine Kate. „Microarray bioinformatics and applications in oncology“. [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2008. http://hdl.handle.net/1765/12618.

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10

Karanam, Suresh Kumar. „Automation of comparative genomic promoter analysis of DNA microarray datasets“. Thesis, Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04062004-164658/unrestricted/karanam%5Fsuresh%5Fk%5F200312%5Fms.pdf.

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Bücher zum Thema "DNA microarrays":

1

Campbell, Marissa J. DNA microarrays, synthesis, and synthetic DNA. Hauppauge, N.Y: Nova Science, 2011.

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2

Muller, Hans-Joachim. Microarrays. Burlington, MA: Elsevier Academic Press, 2005.

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Andreas, Bosio, und Gerstmayer Bernhard, Hrsg. Microarrays in inflammation. Basel: Birkhäuser, 2008.

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4

Muller, Hans-Joachim. Microarrays. Burlington, MA: Elsevier Academic Press, 2006.

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5

Knudsen, Steen. Cancer Diagnostics with DNA Microarrays. New York: John Wiley & Sons, Ltd., 2006.

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6

Dufva, Martin, Hrsg. DNA Microarrays for Biomedical Research. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-538-1.

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7

Knudsen, Steen. Cancer Diagnostics with DNA Microarrays. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0470041102.

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8

Jordan, B. R., Hrsg. DNA Microarrays: Gene Expression Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5.

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9

Baldi, Pierre. DNA microarrays and gene expression. New York: Cambridge University Press, 2002.

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Matsunaga, Tadashi. DNA chippu ōyō gijutsu: DNA chips and it's applications. Tōkyō: Shīemushī, 2000.

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Buchteile zum Thema "DNA microarrays":

1

Dufva, Martin, und Claus B. V. Christensen. „Optimization of Oligonucleotide DNA Microarrays“. In Microarrays, 93–103. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-303-5_4.

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2

Deisingh, Anil K., Adilah Guiseppi-Wilson und Anthony Guiseppi-Elie. „Biochip Platforms for DNA Diagnostics“. In Microarrays, 271–97. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-72719-6_14.

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3

Nguyen, Bao, und Samuel K. Kassegne. „DNA Microarrays“. In Encyclopedia of Microfluidics and Nanofluidics, 628–34. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-5491-5_350.

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4

Nguyen, Bao, und Samuel K. Kassegne. „DNA Microarrays“. In Encyclopedia of Microfluidics and Nanofluidics, 1–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27758-0_350-2.

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5

Bähler, Jürg, und Samuel Marguerat. „DNA Microarrays“. In Encyclopedia of Systems Biology, 609–10. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_743.

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6

Bier, Frank F., Markus von Nickisch-Rosenegk, Eva Ehrentreich-Förster, Edda Reiß, Jörg Henkel, Rothin Strehlow und Dennie Andresen. „DNA Microarrays“. In Biosensing for the 21st Century, 433–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/10_2007_087.

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7

Nguyen, C., und X. Gidrol. „DNA Microarrays“. In Nanoscience, 911–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88633-4_17.

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8

Shi, Leming, Roger G. Perkins und Weida Tong. „The Current Status of DNA Microarrays“. In Microarrays, 3–24. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-72719-6_1.

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9

Pritchard, Clare, Peter Underhill und Andy Greenfield. „Using DNA Microarrays“. In METHODS IN MOLECULAR BIOLOGY™, 605–29. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-483-8_41.

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10

Stoevesandt, Oda, Mingyue He und Michael J. Taussig. „Protein Microarrays Printed from DNA Microarrays“. In Methods in Molecular Biology, 95–106. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-59745-551-0_4.

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Konferenzberichte zum Thema "DNA microarrays":

1

Martins, Diogo, Xi Wei, Rastislav Levicky und Yong-Ak Song. „Accelerating the Mass Transport of DNA Biomolecules Onto DNA Microarray for Enhanced Detection by Electrokinetic Concentration in a Microfluidic Chip“. In ASME 2016 5th International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/mnhmt2016-6562.

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Morpholinos (MOs) are synthetic nucleic acids analogues with a non-charged backbone of morpholine rings. To enhance the MO-DNA hybridization assay speed, we propose the integration of a MO microarray with an ion concentration polarization (ICP) based microfluidic concentrator. The ICP concentrator collects target biomolecules from a ∼μL fluidic DNA sample and concentrates them electrokinetically into a ∼nL plug located in the vicinity of the MO probes. ICP preconcentration not only reduces the analyte diffusion length but also increases the binding reaction rate, and as a result, ICP-enhanced MO microarrays allow much faster hybridization than standard diffusion-limited MO microarrays.

2

ESCANDE, DENIS G. „DNA MICROARRAYS AND ARRHYTHMIAS“. In Proceedings of the 31st International Congress on Electrocardiology. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702234_0079.

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3

Carlon, Enrico. „Thermodynamics of DNA microarrays“. In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-13.

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4

Sheikh, Mona A., Olgica Milenkovic und Richard G. Baraniuk. „Designing Compressive Sensing DNA Microarrays“. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. IEEE, 2007. http://dx.doi.org/10.1109/camsap.2007.4497985.

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5

Turkay, Cagatay, Julius Parulek und Helwig Hauser. „Dual analysis of DNA microarrays“. In the 12th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2362456.2362489.

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6

Jacak, Jaroslaw, Jan Hesse, Clemens Hesch, Maria Kasper, Fritz Aberger, Annemarie Frischauf, Max Sonnleitner, Guenter Freudenthaler, Stefan Howorka und Gerhard J. Schuetz. „Ultrasensitive DNA detection on microarrays“. In Biomedical Optics 2005, herausgegeben von Dan V. Nicolau, Joerg Enderlein, Robert C. Leif, Daniel L. Farkas und Ramesh Raghavachari. SPIE, 2005. http://dx.doi.org/10.1117/12.590476.

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7

Hong, Bong Jin, und Joon Won Park. „DNA microarrays on a mesospaced surface“. In Optics East, herausgegeben von M. Saif Islam und Achyut K. Dutta. SPIE, 2004. http://dx.doi.org/10.1117/12.569677.

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8

Dai, Wei, Olgica Milenkovic, Mona A. Sheikh und Richard G. Baraniuk. „Probe Design for Compressive Sensing DNA Microarrays“. In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.56.

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Parikh, Samir, Glenn Gulak und Paul Chow. „A CMOS Image Sensor for DNA Microarrays“. In 2007 IEEE 29th Custom Integrated Circuits Conference. IEEE, 2007. http://dx.doi.org/10.1109/cicc.2007.4405854.

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10

Vikalo, H., F. Parvaresh, S. Misra und B. Hassibi. „Sparse measurements, compressed sampling, and DNA microarrays“. In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517676.

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Berichte der Organisationen zum Thema "DNA microarrays":

1

Beer, N., B. Baker, T. Piggott, S. Maberry, C. Hara, J. DeOtte, W. Benett, E. Mukerjee, J. Dzenitis und E. Wheeler. Hybridization and Selective Release of DNA Microarrays. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1033734.

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2

Martin, Jennifer A., Yaroslav Chushak, Jorge C. Benavides, Joshua Hagen und Nancy Kelley-Loughnane. DNA Microarrays for Aptamer Identification and Structural Characterization. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada597207.

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Gregory Stephanopoulos. Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring. Office of Scientific and Technical Information (OSTI), Juli 2004. http://dx.doi.org/10.2172/837870.

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4

Ellner, J. J., N. D. Connell, G. Gallagher und E. Raveche. Use of DNA Microarrays to Identify Diagnostic Signature Transcriptional Profiles for Host Responses to Infectious Agents. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2005. http://dx.doi.org/10.21236/ada449913.

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5

Ellner, Jerrold J. Use of DNA Microarrays to Identify Diagnostic Signature Transcription Profiles for Host Responses to Infectious Agents. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2004. http://dx.doi.org/10.21236/ada434780.

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6

Ellner, Jerrold J. Use of DNA Microarrays to Identify Diagnostic Signature Transcriptional Profiles for Host Responses to Infectious Agents. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2003. http://dx.doi.org/10.21236/ada419551.

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7

WERNER-WASHBURNE, MARGARET, und GEORGE S. DAVIDSON. DNA Microarray Technology. Office of Scientific and Technical Information (OSTI), Januar 2002. http://dx.doi.org/10.2172/791894.

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8

Fromm, A., Avihai Danon und Jian-Kang Zhu. Genes Controlling Calcium-Enhanced Tolerance to Salinity in Plants. United States Department of Agriculture, März 2003. http://dx.doi.org/10.32747/2003.7585201.bard.

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Annotation:

The specific objectives of the proposed research were to identify, clone and characterize downstream cellular target(s) of SOS3 in Arabidopsis thaliana, to analyze the Ca2+-binding characteristics of SOS3 and the sos3-1 mutant and their interactions with SOS3 cellular targets to analyze the SOS3 cell-specific expression patterns, and its subcellular localization, and to assess the in vivo role of SOS3 target protein(s) in plant tolerance to salinity stress. In the course of the study, in view of recent opportunities in identifying Ca2+ - responsive genes using microarrays, the group at Weizmann has moved into identifying Ca2+-responsive stress genes by using a combination of aqeuorin-based measurements of cytosolic Ca and analysis by DNA microarrays of early Ca-responsive genes at the whole genome level. Analysis of SOS3 (University of Arizona) revealed its expression in both roots and shoots. However, the expression of this gene is not induced by stress. This is reminiscent of other stress proteins that are regulated by post-transcriptional mechanisms such as the activation by second messengers like Ca. Further analysis of the expression of the gene using promoter - GUS fusions revealed expression in lateral root primordial. Studies at the Weizmann Institute identified a large number of genes whose expression is up-regulated by a specific cytosolic Ca burst evoked by CaM antagonists. Fewer genes were found to be down-regulated by the Ca burst. Among the up-regulated genes many are associated with early stress responses. Moreover, this study revealed a large number of newly identified Ca-responsive genes. These genes could be useful to investigate yet unknown Ca-responsive gene networks involved in plant response to stress.

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Wu, Chi-Fang, James J. Valdes, Jennifer W. Sekowski und William E. Bentley. Identification of Multiple Pathogenic Bacteria Using a DNA Microarray. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2002. http://dx.doi.org/10.21236/ada408810.

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10

O'Malley, Karen L. Fundamental Patterns Underlying Neurotoxicity Revealed by DNA Microarray Expression Profiling. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada409422.

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