Target Discovery and Validation Reviews and Protocols

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Harnessing the Human Genome 25 Fig. 6. Mechanism of the SIM2-s antisense from the GeneChip analysis. RKO colon carcinoma cells were treated with SIM2-s antisense and at the indicated time, total RNA was analyzed for a global gene expression profile with an Affymetrix U133 A and B microarray. The GeneChip output from U133A (known genes) was used to build the preliminary model as shown. 39,000 transcripts derived from approx 33,000 well-substantiated human genes. The chip output was subjected to statistical filtering (100% concordance of the hits from chip and biological replication), p values (>0.05) and -fold changes (>2-fold). The filtered hits were subjected to gene ontology by using GeneSpring (http://www.silicongenetics.com), and a list of genes belonging to distinct families was generated. From the pattern of the unique gene expression profile of the antisense-treated cells, a preliminary working model was generated (Fig. 6). In general, a cascade of gene expression changes was seen that included early perturbation of signal transduction molecules and transcription factors. This cascade was followed by induction of differentiation signals such as leukemia inhibitory factor and growth differentiation factor. In addition, a key apoptotic signal (FasApo) was activated. Induction of differentiation markers succeeded the differentiation signals. At a later time-point, the SIM2-s antisense caused activation of proapoptotic genes and downregulation of proteases and tumor p53. This was accompanied by downregulation of cell cycle genes. At 24 h of treatment with the SIM2-s antisense, signs of stress were apparent, indicated by upregulation of stress response genes; this upregulation was accompanied by a downregulation of metabolic enzymes.
26 Narayanan Fig. 7. DDD filter to identify SIM2-s stimulated and repressed genes. SIM2-s-positive libraries (colon, pancreas, and prostate tumor-derived) were included in pool A and SIM2-s-negative libraries (breast, ovary, and lung tumor-derived) were included in pool B. The DDD tool was used to identify SIM2-s coexpressed targets. Efforts are currently underway to identify direct targets regulated by SIM2-s by means of bioinformatics approaches by using DDD (Fig. 7). The basis for this filter is that genes expressed in tumors where SIM2-s is activated (coexpressed genes) must encompass the putative SIM2-s targets. The SIM2-s-positive (pool A) cDNA UniGene libraries can be compared with SIM2-s negative (pool B) libraries from the CGAP database by using the DDD tool from CGAP to identify SIM2-s coexpressing genes. Genes that are elevated in SIM2-s-positive libraries (A>B) are predicted to encompass SIM2-s-stimulated genes. Correspondingly, genes that are downregulated in the SIM2-s-positive libraries (B>A) are predicted to encompass potential SIM2-s-repressed genes. Among the A>B list of 581 genes, key apoptotic, survival, signal transduction molecules as well as SIM2-s were identified, suggesting a potential use of this filter. The antisense fingerprint of genes from the time-course experiments can be compared using this DDD filter. Comparison of A>B output with the fingerprint of antisense downregulated genes from the chip output is predicted to identify SIM2-s-stimulated targets. Conversely, comparison of B>A output with the fingerprint of antisense upregulated genes is predicted to identify SIM2-s
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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76 Beaty et al. The Following Volum
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78 Beaty et al. using a protein ass
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82 Beaty et al. Fig. 2. Preparation
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86 Beaty et al. 3. Kern, S. E., Hru
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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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146 Sano and Taira 5. 10X L (low-sa
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148 Sano and Taira ribozymes may cl
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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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182 Houdebine Fig.7. Example of a v
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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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190 Houdebine of the intestinal epi
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192 Houdebine interference of the g
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194 Houdebine 17. Whitelaw, C. B. A
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196 Houdebine 51. Bell, A. C., West
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198 Houdebine 86. Carmichael, G. G.
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200 Houdebine 121. Pailhoux, E., Vi
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202 Houdebine 156. Auerbach, A. B.,
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204 Vijayaraj, Söhl, and Magin 1.
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206 Vijayaraj, Söhl, and Magin Fig
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208 Vijayaraj, Söhl, and Magin fil
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212 Table 2 Orthologous Human and M
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242 Table 3 Time Schedule For Aggre
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250 Vijayaraj, Söhl, and Magin 101
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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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270 Iversen and Sørensen witnessed
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274 Iversen and Sørensen the core
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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 293 (unre
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Overview of Immune System 299 (44).
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Overview of Immune System 301 demon
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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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