Microarrays Princípios e Potencial

www.lge.ibi.unicamp.br

MicroarraysPrincípios e Potencial

PG- Bioquímica - 23/09/03


Genoma ????

Moléculas que guardam a informação/instrução hereditária de uma entidade biológica replicante

Vírus

DNA RNA

Procariotos(Bactérias e Archea)

Eucariotos(Protozoários, Fungos, Plantas e Animais)

M

C

A


O Oitavo dia da Criação

Membrana

Citoplasma

Núcleo

DNA

INFORMAÇÃO


Propriedades do DNA

Princípio do Alfabeto

PareamentoA T

GC

Vírus: n X 1000 bp, ~10 – 100 genesBactérias: 2 – 6 Mb, ~ 2 – 5.000 genesFungo: 10 – 50 Mb, ~ 6 – 20.000 genesProtozoários: 20 – 100 Mb, ~ 5 – 20.000Plantas: 100 – X Mb, > ~ 10.000Homem: 3Bi bp, ~ 30.000 genes

5’-ATGCCT-3’ 5’-TCCGTA-3’

AROMA AMORA


TATAAA....................TACACACAG...........ACT..........ATATTT.....................ATGTGTGTC...........TGA..........

Genes: Estrutura e Função

Proteína ~ FENÓTIPO

Controle

OFF

ON

Informação

...AUG UGU GUC........UGA......

mRNA/tRNA/rRNA


Célula

Comando

Informática celular

Trilhões de computadores idênticos executando diferentes

combinações de comandos

Programa Celular

VIDA


Genomas

Seqüenciamento

1990: 50kb/ano2000: 50kb/hora

Organismo Gene

BioquímicaDescobre a função do Gene A

no Organismo X

GenômicaDescobre que o Organismo Y possui um gene

semelhante ao Gene A do Organismo X


Missão da Genômica

Genômica X

Tentativa e Erro

Hipóteses para definição de ALVOS


Ferramentas da Genômica

Organismo

Seqüência Bruta

Genes

Programação Celular

Seqüenciamento

Bioinformática

Análise de Expressão

www.lge.ibi.unicamp.br Histórico

Genomics

1990-50kb/ano2000-50kb/hora

Organismo Gene

ONSA

Organization for NucleotideSequence and Analisys

www.lge.ibi.unicamp.brMetabolismo Virtual

www.lge.ibi.unicamp.brGenoma

Humano do Câncer

N T

AAAAAAAAAAAA

AAAAAAAAAAAA

AAAAAA

Oligos

PCR

Seqüenciamento

Análise:Bancos de Dados

Gene humano

Gene não humano

No match

InteresseFatores de transcriçãoAnti-oxidantes

Bioinformática

Grupo CM4/ CM

www.lge.ibi.unicamp.brImportance of Brazilian

Human Genome

Annotation

PNAS. 2000 Nov 7;97(23):12690-3.

AAAAAA

Most EST projects

Orestes

Biological Information

Orestes Library

BioinformaticsSelection tools

ResearchGroups

(cancers, diabetes,hypertension, etc.)


Artéria

Cérebro

Câncer

Intestino

www.lge.ibi.unicamp.brChips de DNA

Reprogramação celular1 cm2

A B C D E

A B C D E

DNA

iRNA

cRNA

Microarrays

www.lge.ibi.unicamp.brDetalhes experimentais

www.lge.ibi.unicamp.brDetalhes experimentais


Exploring the Metabolic and Genetic Control ofGene Expression on a Genomic Scale

Science, 278: 680 (1997) Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown *


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*,




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




Systematic changes in gene expression patternsfollowing adaptive evolution in yeast

PNAS 96:9721 (1999)

Tracy L. Ferea*, David Botstein*, Patrick O. Brown,, and R. FrankRosenzweig,§



Figure 1. The same section of the microarray is shown for three independent hybridizations comparing RNA isolated at the8-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 preparecDNA labeled with Cy3-deoxyuridine triphosphate (dUTP), and mRNA harvested from cells at different times after serumstimulation was used to prepare cDNA labeled with Cy5-dUTP. The two cDNA probes were mixed and simultaneouslyhybridized to the microarray. The image of the subsequent scan shows genes whose mRNAs are more abundant in theserum-deprived fibroblasts (that is, suppressed by serum treatment) as green spots and genes whose mRNAs are moreabundant in the serum-treated fibroblasts as red spots. Yellow spots represent genes whose expression does not varysubstantially between the two samples. The arrows indicate the spots representing the following genes: 1, protein disulfideisomerase-related protein P5; 2, IL-8 precursor; 3, EST AA057170; and 4, vascular endothelial growth factor.

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 2. Cluster image showing the different classes of gene expression profiles. Five hundred seventeen genes whose mRNAlevels changed in response to serum stimulation were selected (7). This subset of genes was clustered hierarchically into groupson the basis of the similarity of their expression profiles by the procedure of Eisen et al. (6). The expression pattern of eachgene in this set is displayed here as a horizontal strip. For each gene, the ratio of mRNA levels in fibroblasts at the indicatedtime 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 expressionprofiles 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 haveidentical or very similar expression profiles, and the profiles corresponding to these independent measurements clustered eitheradjacent or very close to each other, pointing to the robustness of the clustering algorithm in grouping genes with very similarpatterns of expression.


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


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 variousaspects of the wound-healing process and fibroblast proliferation are shown with the same convention for representing changesin 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) unidentifiedrole in wound healing, and (I) cholesterol biosynthesis. The numbers in (C) and (G) refer to genes whose products serve assignals to neutrophils (C1), monocytes and macrophages (C2), T lymphocytes (C3), B lymphocytes (C4), and melanocytes(G1).

www.lge.ibi.unicamp.brEnvelhecimento

Tempo

70%

Tempo

100%


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 muscleof mice. Use of high-density oligonucleotide arrays representing 6347 genesrevealed that aging resulted in a differential gene expression pattern indicative of amarked stress response and lower expression of metabolic and biosyntheticgenes. Most alterations were either completely or partially prevented by caloricrestriction, the only intervention known to retard aging in mammals.Transcriptional patterns of calorie-restricted animals suggest that caloric restrictionretards the aging process by causing a metabolic shift toward increased proteinturnover and decreased macromolecular damage.



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*

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 determinethe class of new leukemia cases. The resultsdemonstrate the feasibility of cancer classification based solelyon gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Science, Volume 286, Number 5439 Issue of 15 Oct 1999, pp. 531 - 537


Although the distinction between AML and ALL has been well established, no single test is currently sufficient to establish thediagnosis. Rather, current clinical practice involves an experienced hematopathologist's interpretation of the tumor'smorphology, histochemistry, immunophenotyping, and cytogenetic analysis, each performed in a separate, highly specializedlaboratory. 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 containcorticosteroids, vincristine, methotrexate, and L-asparaginase, whereas most AML regimens rely on a backbone ofdaunorubicin and cytarabine (8). Although remissions can be achieved using ALL therapy for AML (and vice versa), cure ratesare markedly diminished, and unwarranted toxicities are encountered.


www.lge.ibi.unicamp.brGrupo de Ecologia Molecular


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Microarrays Princípios e Potencial