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Microarrays Princípios e Potencial

Microarrays Princípios e Potencial. PG- Bioquímica - 23/09/03. DNA. RNA. M. C. A. Genoma ????. Moléculas que guardam a informação/instrução hereditária de uma entidade biológica replicante. Vírus. Eucariotos (Protozoários, Fungos, Plantas e Animais). Procariotos

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Microarrays Princípios e Potencial

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  1. Microarrays Princípios e Potencial PG- Bioquímica - 23/09/03

  2. DNA RNA M C A Genoma ???? Moléculas que guardam a informação/instrução hereditária de uma entidade biológica replicante Vírus Eucariotos (Protozoários, Fungos, Plantas e Animais) Procariotos (Bactérias e Archea)

  3. INFORMAÇÃO O Oitavo dia da Criação Membrana Citoplasma DNA Núcleo

  4. Propriedades do DNA Princípio do Alfabeto 5’-ATGCCT-3’ 5’-TCCGTA-3’ AROMA AMORA Vírus: n X 1000 bp, ~10 – 100 genes Bactérias: 2 – 6 Mb, ~ 2 – 5.000 genes Fungo: 10 – 50 Mb, ~ 6 – 20.000 genes Protozoários: 20 – 100 Mb, ~ 5 – 20.000 Plantas: 100 – X Mb, > ~ 10.000 Homem: 3Bi bp, ~ 30.000 genes T A Pareamento C G

  5. TATAAA....................TACACACAG...........ACT.......... ATATTT.....................ATGTGTGTC...........TGA.......... ...AUG UGU GUC........UGA...... mRNA/tRNA/rRNA Genes: Estrutura e Função Controle Informação OFF ON Proteína ~ FENÓTIPO

  6. Célula VIDA Informática celular Trilhões de computadores idênticos executando diferentes combinações de comandos Programa Celular Comando

  7. Genomas Seqüenciamento 1990: 50kb/ano 2000: 50kb/hora Organismo Gene Bioquímica Descobre a função do Gene A no Organismo X Genômica Descobre que o Organismo Y possui um gene semelhante ao Gene A do Organismo X

  8. Missão da Genômica Genômica X Tentativa e Erro Hipóteses para definição de ALVOS

  9. Seqüenciamento Bioinformática Análise de Expressão Ferramentas da Genômica Organismo Seqüência Bruta Genes Programação Celular

  10. Histórico ONSA Genomics 1990-50kb/ano 2000-50kb/hora Organismo Gene Organization for Nucleotide Sequence and Analisys

  11. Metabolismo Virtual

  12. T N Bioinformática AAAAAA AAAAAA AAAAAA AAAAAA AAAAAA Gene humano Oligos Gene não humano No match PCR Genoma Humano do Câncer Seqüenciamento Análise: Bancos de Dados Grupo CM4/ CM Interesse Fatores de transcrição Anti-oxidantes

  13. Bioinformatics Selection tools AAAAAA Most EST projects Research Groups (cancers, diabetes, hypertension, etc.) Orestes Importance of Brazilian Human Genome Annotation Biological Information Orestes Library PNAS. 2000 Nov 7;97(23):12690-3.

  14. Intestino Artéria Cérebro Câncer

  15. A A B B C C D D E E DNA iRNA cRNA 1 cm2 Chips de DNA Reprogramação celular Microarrays

  16. Detalhes experimentais

  17. Detalhes experimentais

  18. Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale Science, 278: 680 (1997) Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown *

  19. Cluster analysis and display of genome-wide expression patterns PNAS 95:14863 (1998) Michael B. Eisen*, Paul T. Spellman*, Patrick O. Brown, and David Botstein*,

  20. The Transcriptional Program of Sporulation in Budding Yeast Science 282:699 (1998) S. Chu, * J. DeRisi, * M. Eisen, J. Mulholland, D. Botstein, P. O. Brown, I. Herskowitz

  21. Systematic changes in gene expression patterns following adaptive evolution in yeast PNAS 96:9721 (1999) Tracy L. Ferea*, David Botstein*, Patrick O. Brown,, and R. Frank Rosenzweig,§

  22. The Transcriptional Program in the Response of Human Fibroblasts to Serum Science 283: 83 (1999) Vishwanath R. Iyer, et al 8600 different human genes. Genes could be clustered into groups on the basis of their temporal patterns of expression in this program. Many features of the transcriptional program appeared to be related to the physiology of wound repair, suggesting that fibroblasts play a larger and richer role in this complex multicellular response than had previously been appreciated. Figure 1. The same section of the microarray is shown for three independent hybridizations comparing RNA isolated at the 8-hour time point after serum treatment to RNA from serum-deprived cells. Each microarray contained 9996 elements, including 9804 human cDNAs, representing 8613 different genes. mRNA from serum-deprived cells was used to prepare cDNA labeled with Cy3-deoxyuridine triphosphate (dUTP), and mRNA harvested from cells at different times after serum stimulation was used to prepare cDNA labeled with Cy5-dUTP. The two cDNA probes were mixed and simultaneously hybridized to the microarray. The image of the subsequent scan shows genes whose mRNAs are more abundant in the serum-deprived fibroblasts (that is, suppressed by serum treatment) as green spots and genes whose mRNAs are more abundant in the serum-treated fibroblasts as red spots. Yellow spots represent genes whose expression does not vary substantially between the two samples. The arrows indicate the spots representing the following genes: 1, protein disulfide isomerase-related protein P5; 2, IL-8 precursor; 3, EST AA057170; and 4, vascular endothelial growth factor.

  23. Figure 2. Cluster image showing the different classes of gene expression profiles. Five hundred seventeen genes whose mRNA levels changed in response to serum stimulation were selected (7). This subset of genes was clustered hierarchically into groups on the basis of the similarity of their expression profiles by the procedure of Eisen et al. (6). The expression pattern of each gene in this set is displayed here as a horizontal strip. For each gene, the ratio of mRNA levels in fibroblasts at the indicated time after serum stimulation ("unsync" denotes exponentially growing cells) to its level in the serum-deprived (time zero) fibroblasts is represented by a color, according to the color scale at the bottom. The graphs show the average expression profiles for the genes in the corresponding "cluster" (indicated by the letters A to J and color coding). In every case examined, when a gene was represented by more than one array element, the multiple representations in this set were seen to have identical or very similar expression profiles, and the profiles corresponding to these independent measurements clustered either adjacent or very close to each other, pointing to the robustness of the clustering algorithm in grouping genes with very similar patterns of expression.

  24. Figure 4. "Reprogramming" of fibroblasts. Expression profiles of genes whose function is likely to play a role in the reprogramming phase of the response are shown with the same representation as in Fig. 2. In the cases in which a gene was represented by more than one element in the microarray, all measurements are shown. The genes were grouped into categories on the basis of our knowledge of their most likely role. Some genes with pleiotropic roles were included in more than one category.

  25. Figure 5. The transcriptional response to serum suggests a multifaceted role for fibroblasts in the physiology of wound healing. The features of the transcriptional program of fibroblasts in response to serum stimulation that appear to be related to various aspects of the wound-healing process and fibroblast proliferation are shown with the same convention for representing changes in transcript levels as was used in Figs. 2 and 4. (A) Cell cycle and proliferation, (B) coagulation and hemostasis, (C) inflammation, (D) angiogenesis, (E) tissue remodeling, (F) cytoskeletal reorganization, (G) reepithelialization, (H) unidentified role in wound healing, and (I) cholesterol biosynthesis. The numbers in (C) and (G) refer to genes whose products serve as signals to neutrophils (C1), monocytes and macrophages (C2), T lymphocytes (C3), B lymphocytes (C4), and melanocytes (G1).

  26. Tempo 100% Tempo Envelhecimento 70%

  27. Gene Expression Profile of Aging and Its Retardation by Caloric Restriction Cheol-Koo Lee, 1,3 Roger G. Klopp, 2 Richard Weindruch, 4* Tomas A. Prolla 3* Science, Volume 285, Number 5432 Issue of 27 Aug 1999, pp. 1390 - 1393 The gene expression profile of the aging process was analyzed in skeletal muscle of mice. Use of high-density oligonucleotide arrays representing 6347 genes revealed that aging resulted in a differential gene expression pattern indicative of a marked stress response and lower expression of metabolic and biosynthetic genes. Most alterations were either completely or partially prevented by caloric restriction, the only intervention known to retard aging in mammals. Transcriptional patterns of calorie-restricted animals suggest that caloric restriction retards the aging process by causing a metabolic shift toward increased protein turnover and decreased macromolecular damage.

  28. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring T. R. Golub, 1,2* D. K. Slonim, 1 P. Tamayo, 1 C. Huard, 1 M. Gaasenbeek, 1 J. P. Mesirov, 1 H. Coller, 1 M. L. Loh, 2 J. R. Downing, 3 M. A. Caligiuri, 4 C. D. Bloomfield, 4 E. S. Lander 1,5* Science, Volume 286, Number 5439 Issue of 15 Oct 1999, pp. 531 - 537 Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The resultsdemonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

  29. Although the distinction between AML and ALL has been well established, no single test is currently sufficient to establish the diagnosis. Rather, current clinical practice involves an experienced hematopathologist's interpretation of the tumor's morphology, histochemistry, immunophenotyping, and cytogenetic analysis, each performed in a separate, highly specialized laboratory. Although usually accurate, leukemia classification remains imperfect and errors do occur. Distinguishing ALL from AML is critical for successful treatment; chemotherapy regimens for ALL generally contain corticosteroids, vincristine, methotrexate, and L-asparaginase, whereas most AML regimens rely on a backbone of daunorubicin and cytarabine (8). Although remissions can be achieved using ALL therapy for AML (and vice versa), cure rates are markedly diminished, and unwarranted toxicities are encountered.

  30. Grupo de Ecologia Molecular

  31. Micro-bacia III Micro-bacia I Micro-bacia II

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