Target Discovery and Validation Reviews and Protocols

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Gene Networks 35 Fig. 1. Conceptual view of the identification of drug-affected and druggable genes (11). Fig. 2. Conceptual view of the identification of drug-active pathways (13). in method 1. The drug-active pathways are identified as the subnetworks of the estimated gene network. The information of time dependency on drug response time-course data is used for reducing the number of falsely identified pathways. By highlighting the genes on the network for each time-point, we can observe the flow of the drug responses of genes in the network. We demonstrate the whole process of our method for identifying the drugaffected genes, the druggable genes, and the drug-active pathways by using 120 disruptant microarray gene expression data and drug response time-course microarray data for an antifungal drug, griseofulvin, for Saccharomyces cerevisiae.
36 Imoto et al. 2. Materials Two types of cDNA microarray measurements of S. cerevisiae have been prepared for our purpose. One type is the microarray data obtained by gene disruptions, and the other type is time-course data of responses to an antifungal medicine. The number of disruptants is 120 for the first type of data, and the time-course data for one dose consists of five microarrays. 2.1. Microarray Data of Single Gene Disruptions The first microarray data are obtained by disrupting 120 genes, where mostly transcription factors are disrupted. The BY4741 (MATa, HIS3D1, LEU2D0, MET15D0, URA3D0) is used as the wild-type strain, and purchased gene disruptions were from Research Genetics (Huntsville, AL). The disrupted genes are listed in Table 1. To monitor the gene expression profile, cells were inoculated and grown in YPD medium (1% yeast extract, 1% bacto-peptone, 2% glucose) at 30ºC until optical density600 reached 1.0 in the logarithmic growth phase, and then cells were harvested to isolate mRNA for assay of gene expression. The respective parental strain was the control used for each disruptant strain. See Aburatani et al. (14) for details. The total RNAs are extracted from these experiments and labeled by Cy5. These Cy5-labeled RNAs are hybridized with Cy3-labeled RNAs from nontreated cells. A microarray then measures 5871 gene expression levels at once. 2.2. Drug Response Time-Course Microarray Data To determine the previously unknown underlying molecular affects of the popular generic antifungal agent griseofulvin, we created time-course drug response microarray data. Griseofulvin is a widely prescribed oral antifungal agent that is indicated primarily for severe fungal infections of the hair and nails. Although griseofulvin’s molecular action is unknown, it is known that the drug disrupts mitotic structure in fungi, leading to metaphase arrest. We incubated yeast cultures in dosages of 10, 25, 50, and 100 mg/mL antifungal medication in culture and took aliquots of the culture at five time-points (0, 15, 30, 45, and 60 min) after addition of the agent. Here, time 0 means the start point of this observation and just after exposure to the drug. We then extracted the total RNAs from these experiments, labeled the RNAs with Cy5, hybridized them with Cy3-labeled RNAs from nontreated cells, and applied them to full genome cDNA microarrays, thereby creating a data set of 20 microarrays for drug response data. 2.3. Data Normalization It is known that expression profile data measured by microarrays have several systematic biases, such as spotting position-specific bias, intensity dependent
Page 1 and 2: METHODS IN MOLECULAR BIOLOGY 360 T
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Page 7 and 8: vi Preface get. Approaches to targe
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Page 11 and 12: x Contributors SAHOHIME MATSUMOTO
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88 Beaty et al. 34. Peters, D. G.,
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5 Molecular Classification of Breas
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Molecular Profiling of Breast Cance
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Fig. 2. (Continued) the right ident
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6 Discovery of Differentially Expre
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Gene Target Discovery 117 In the fi
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Gene Target Discovery 119 sequences
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Gene Target Discovery 121 There is
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Gene Target Discovery 123 digestion
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Gene Target Discovery 125 Fig. 1. P
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Gene Target Discovery 127 5. Sargen
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Gene Target Discovery 129 41. Berry
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132 Matsumoto, Miyagishi, and Taira
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144 Fig. 1. The ribozyme-expression
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150 Fig. 3. A system for the identi
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152 Sano and Taira 4. Notes 1. We r
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9 Production of siRNA- and cDNA-Tra
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Transfected Cell Arrays 157 Fig. 1.
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Transfected Cell Arrays 159 2. The
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Transfected Cell Arrays 161 5. Simp
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164 Houdebine Fig. 1. Different met
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166 Houdebine concentrations become
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168 Houdebine Fig. 2. Major methods
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170 Houdebine integrate into the me
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172 Houdebine 2.2.3. Knock-In Into
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174 Houdebine as insulators. The co
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176 Houdebine Fig. 5. Different met
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178 Houdebine systems. Moreover, st
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180 Houdebine contribute to generat
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184 Houdebine more extensively. Tra
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186 Houdebine and stroma. Several o
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188 Houdebine explained at the mole
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12 The HUVEC/Matrigel Assay An In V
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256 Skovseth, Küchler, and Haralds
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14 An Overview of the Immune System
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Overview of Immune System 279 diffe
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Overview of Immune System 299 (44).
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Overview of Immune System 307 some
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Overview of Immune System 313 measu
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Overview of Immune System 315 42. H
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Overview of Immune System 317 74. D
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15 Potential Target Antigens for Im
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Tumor Antigen Discovery 321 develop
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Tumor Antigen Discovery 323 panel b
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Tumor Antigen Discovery 325 16. Joc
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16 Identification of Tumor Antigens
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Immunoproteomics 329 by “shot gun
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Immunoproteomics 331 isoelectric fo
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Immunoproteomics 333 3. 2D-PAGE is
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17 Protein Arrays A Versatile Toolb
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Protein Arrays 337 Table 1 Classifi
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Protein Arrays 339 fractions from c
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Protein Arrays 341 combinatorial sc
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Protein Arrays 343 The ideal proteo
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Protein Arrays 345 18. Cekaite, H.
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Protein Arrays 347 49. Yuko Kawahas
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Index 349 Index A Acetyl-CoA carbox
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Index 351 conditional by using FRT
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Index 353 Recombinant tumor antigen
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