Transcriptome analysis using Open Reading frame ESTs (ORESTES)

1. Transcriptome analysis using Open Reading frame ESTs (ORESTES) Emmanuel Dias Neto, PhD Lab of Neurosciences, LIM-27 Instituto de Psiquiatria Faculdade de Medicina Universidade de Sao Paulo, SP - BRAZIL

2. UNESCO - First North-South Human Genome Conference • Caxambú, MG, Brazil - 1993 • Is there a way to integrate the research performed in developing countries with the US/Europe ‘Human Genome Project’ ? • After the completion of the ‘Human Genome sequencing’, how can we gain access or make use of the technology developed ?

4. How can we learn ? • Initiate an EST sequencing project of a parasite of local importance (Schistosoma mansoni) • cDNA libraries prepared with Marcelo Bento Soares • cDNA sequencing performed at TIGR (Craig Venter) • Some 1,000 ESTs generated

5. 4 Kb 5’ 3’ 500 nt Open reading frame (ORF) 500 nt ESTs “Expressed Sequence Tags” Partial sequences, usually derived from the ends of cDNA molecules.

6. Oligo dT primers cDNA Adaptadors

7. vector Insert ~3kb vector Sequencing Primers ESTs (HGP)

8. Main problems found • - Repetitive sequencing of highly expressed genes : high redundancy (~60%) • - Necessity of large amounts of mRNA in order to obtain a normalized library • - Reduced information of no matches

9. Gene expression in a typical eukaryotic cell Diversity <10 500 11.000 Class Abundant Intermediate Rare Abundance/gene 12,000 300 10 Huang et al., 1999

10. Alternative protocol to generate ESTs • Is there a way to tag rare genes ? • How to generate data from small amounts of mRNA ? • Is it possible to tag the central portion of the transcripts ?

11. Ideas • The use of a PCR-based strategy, should enable the analysis of small amounts of mRNA. • Using randomly selected primers (in RT-PCR) at low stringency as a means to evaluate other regions of the transcripts...

12. ORESTES Randomly selected primers

13. Factors that contribute for the presence of a gene in a cDNA library Nucleotide diversity Abundance ORESTES Usual cDNA libraries

14. ORESTES - the data normalization

15. Covering a transcript with ORESTES • The amplification of a gene region requires primer binding at both sides of a point. • The chance of a primer binding, depends on the size of the sequences flanking the amplification point. • If the size of a transcript is taken as 1, and the distance of the 3’ end is taken as S: • The probability (P) of an appropriate amplification of a point is • P = S(1-S) • Coverage of the central point = 0.5(1-0.5) = 0.5x0.5 = 0.25 = 25% • Coverage of the last 10% of a transcript = 0.1x0.9 = 0.09 = 9%

16. Position of matches

17. ORESTES- sequence distribution

18. ORESTES - the data Comparison with dbest data

21. P P P P P P P P P P P P P P P P P P P P P P P P P P P Project Organisation P P P P P UNICAMP P P P P P P P P P P P P Sequencing Center IQ-USP FM-USP/RP Sequencing Center Sequencing Center Coordination LICR P P P Sequencing Center Sequencing Center FM-USP EPM P P P P P P P P P P

22. Project Organisation Dept. of Pathology Hospital A.C. Camargo RNA coordination LICR/SP Library coordination LICR/SP Dissected tissue samples Preparation and validation of all mRNAs to be used • cDNA synthesis • and amplification • ORESTES production and development • ORESTES sequencing

24. Fernando Costa (CM) S é rgio Verjovski (QV) P P Christine Hackel Arthur Gruber P P Helaine Carrer / Dirce Carraro Mari Cleide Sogayar P P Ma F á tima Sonati Edna Kimura P P Gon ç alo G. Pereira Hamza FA El - Dorry P P Maria Aparecida Nagai (MR) Marco Ant ô nio Zago (RC) P P Angelita Gama Enilza Espe á frico P P Daniel Gianella Neto Gustavo H Goldman P P Suely KN Marie Ma Lu í sa Pa çó - Larson P P Elizabeth Martins Paulo L. Hoo Vanderlei Rodrigues P P Eloiza Tajara P Marcelo Briones (PM) Sandro Valentini P P Rui MB Maciel P Luis Eduardo Andrade P Ismael DG Silva P Jo ã o Bosco Pesquero P Maria In ê s Pardini (IL2) Marina N ó brega (IL3) P P S í lvia Rogatto (IL5) P

26. Using ORESTES to help to define the complete set of genes expressed in different human tissues/tumours

27. Generation of Colon ESTs HCGP X CGAP = 2,1x more sequences

28. Generation of Stomach ESTs HCGP X CGAP = 2,5x more sequences

29. Generation of Breast ESTs HCGP X CGAP = 9,1x more sequences

30. Generation of Head and Neck ESTs HCGP X CGAP = 34,4x more sequences

31. Next challenge Data  Information

32. The Head & Neck transcriptome initiative

33. Transcriptional level Tumor Suppressor genes

34. Looking for putative tumour suppressor genes - Clusters composed of sequences exclusively derived from normal samples - Clusters mapping to genomic regions of frequent Loss (LOH) in H&N tumours Total = 78 clusters

35. Transcriptional level Oncogenes

36. Looking for putative oncogenes - Clusters composed of sequences exclusively derived from tumour samples - Clusters mapping to genomic regions frequently amplified in H&N tumours Total = 271 clusters

37. Differential gene expression in Larynx tumors

38. Differential gene expression in Pharynx tumors

39. Differential gene expression in Oral cavity tumors

40. Differential gene expression in Oral cavity tumors

41. Transcriptional level

43. A B C D

44. Gene humano

45. Homo sapiens RAB1, member RAS oncogene family (RAB1), mRNA HSD00365 - TCGTTATGCCAGTGAAAATGTCAACAAATTGTTGGTAGGGAACAAATGTGA RC5-BT0377-030200-012-A06 - .........................a......................... PM2-BT0723-090201-010-c07 - .........................c......................... PM2-BT0723-130900-002-c07 - .........................c......................... MR3-GN0190-301100-004-e08 - .........................c......................... MR4-ET0140-220101-004-d02 - .........................c......................... MR4-EN0075-220101-006-d02 - .........................c......................... IL2-FT0160-070800-121-C02 - .........................a......................... MR4-ET0140-190201-007-h04 - .........................a......................... MR0-RT0037-121200-004-d02 - .........................a......................... CM1-HN0016-161100-568-c06 - .........................a......................... QV3-BN0046-150300-121-a12 - .........................a......................... QV3-DT0045-210100-063-f03 - .........................a......................... QV2-NN0045-220800-323-d03 - .........................a......................... IL5-UM0067-240300-051-g06 - ..............…..........a......................... CM4-HN0021-241100-457-h02 - .........................a......................... MR0-RT0037-011200-002-a07 - .........................a......................... MR2-UM0060-030400-103-g02 - .........................a..............g.......... PM0-IT0018-091100-001-e02 - .........................a......................... PM1-MT0143-101100-003-a06 - .......*.................a......................... PM1-MT0143-101100-003-f11 - .........................a......................... Type Non-Synonymous Codon aaa-caa Nucleotide A-C Aminoacid K(lysine)-Q(glutanine)

47. "You have made your way from worm to man but much within you is still worm"(Friedrich Nietzche, Zarathustra's Prologue)

50. S. japonicum 43,707 ESTs 28,839 adult worms 14,868 eggs