Comparative analysis of eukaryotic genes Mar Albà genomics.imim.es/evolgenome

1. Comparative analysis of eukaryotic genes Mar Albà http://genomics.imim.es/evolgenome Barcelona Biomedical Research Park

2. Genome Projects GOLD: Genomes Online Database (www.genomesonline.org)

3. Genome Projects GOLD: Genomes Online Database (www.genomesonline.org)

4. Genome Projects GOLD: Genomes Online Database (www.genomesonline.org)

5. Genome Browsers • NCBI Map Viewer • http://www.ncbi.nlm.nih.gov/mapview/ • Ensembl • http://www.ensembl.org • -UCSC Genome Browser • http://genome.cse.ucsc.edu The three databases use the same genome assembly, which is generated by NCBI.

6. Ensembl

7. Ensembl • genomic regions • alignments with synthenic sequences • genes • Homologs, SNPs • transcripts • EMBL mRNAS, ESTs, Expression • proteins • Gene Ontology (function), protein domains, disease • associations

8. Ensembl - Biomart - retrieval of information on gene datasets

9. coding promoter species 1 x N genes (orthologs) species 2 ... species m Gene comparative sequence analysis Genome and transcriptome projects have generated a vast amount of information on protein-coding and non-coding gene sequences. Identification of conserved sequences in different genes can help us understand gene evolution and identify functional regions.

10. Non-coding sequences in vertebrate genomes • only 1.2% of the human genome codes for proteins • but 5% exhibits high sequence conservation levels, • compatible with negative selection (MGSC, 2002) • non-coding • Transcription regulatory regions • Introns • Non-protein coding exons/genes (miRNAs, etc.) • Repetitive elements (Alus, etc.) • Ultra-conserved elements

11. Gene transcription regulatory sequences Maston et al., 2006 Annu. Rev. Genomics Hum. Genet. 7: 29-59

12. Frequently-found metazoan motifs in the core promoter Maston et al., 2006

13. Eukaryotic promoter diversity Wray et al. (2003), Mol. Biol. Evol. 20(9):1377-1419.

14. High evolvability of regulatory sequences • most of the changes in regulatory networks are likely to • occur in cis; changes in trans (transcription factors) may • often have too strong effects. • one single mutation may lead to the acquisition of a new • DNA-factor interaction (rapid turnover) • the expression in one tissue may evolve independently of • expression in another tissue (promoter modular organization) Wray et al. (2003) The Evolution of Transcriptional Regulation in Eukaryotes. Mol. Biol. Evol. 20(9):1377-1419.

15. Transcription factor binding sites (TFBS) are short and imprecise • short sequence motifs (6-12 bp) • some positions of the motif are variable • sometimes different transcription factors can recognize the same sequence motif TATAAA TATAGA TATAAA TATAAA GATAAA TATAAA TATAAA TATAAT *** TATA box

16. Transcription factor binding sites (TFBS) Weight matrices TATAAA TATAGA TATAAA TATAAA GATAAA TATAAA TATAAA TATAAT *** 1 2 3 4 5 6 - - - - - - - - - - - - A 0 8 0 8 7 7 C 0 0 0 0 0 0 G 1 0 0 0 1 0 T 7 0 8 0 0 1 -> can be used to search for putative motifs in sequences

17. Transcription factor binding site databases Place http://www.dna.affrc.go.jp/htdocs/PLACE/ TRANSFAC http://transfac.gbf.de/TRANSFAC/ http://www.biobase.de SCPD http://cgsigma.cshl.org/jian/ TRRD http://www.bionet.nsc.ru/trrd/ RegulonDB http://regulondb.ccg.unam.mx/ ooTFD / rTFD http://www.ifti.org/cgi-bin/ifti/ootfd.pl

18. TFBS prediction using weight matrices PROMO http://promo.lsi.upc.edu Farré, D., et al. (2003). Nucleic Acids Research 31: 1739-1748.

19. High false positive rate in TFBS prediction Test Sequences:200 vertebrate promoter sequences 607 experimentally-verified sites Predictions: Transfac v.6.4 SENSITIVITY: 46% SPECIFICITY: 2% Very low! Blanco, E., et al.. (2006). Nucleic Acids Research 34: D63-D67.

20. Comparative approaches are necessary Select those motifs or regions that are shared by: • orthologous sequences : phylogenetic footprinting • co-expressed genes : shared regulatory motifs

21. Phylogenetic footprinting Boffelli D, Nobrega MA, Rubin EM. (2004) Nat Rev Genet. 5:456-65

22. Phylogenetic footprinting Highly conserved enhancer in gene DACH1

23. Proximal promoter pre-initiation complex

24. Motif positional bias Signal Search Analysis Server (SIB)

25. proximal promoter regulatory module PIC TFB 2 TFBS 1 TFBS 1 TF1 TF2 ACT TSS Why some motifs should show positional bias? • promoter structure • protein-protein interaction positional constraints Predicted element Reference element (known)

26. PEAKS: identification of motif positional bias Predicted element Reference element (known) over-representation TFBS TSS functionally-related sequences (ex. co-expressed) random

27. PEAKS Step 1. Construct motif frequency profile sliding window 3 profile 2 2 1 1 1 1 seq1 seq2 seq3 seq4 Predicted element Reference element (known)

28. PEAKS Step 1. Construct motif frequency profile TSS 308 housekeeping genes Transfac v.6.4 matrix library

29. PEAKS Step 2. Measure significance of peaks For each matrix: maximum peak difference average signal +675 -325 CAAT-box Score (max peak) = Sa x Sb x Sc Sa = max peak / num motif Sb = max peak / num seq Sc = max peak / average num motifs

30. PEAKS Step 2. Measure significance of peaks • determine random expectation score cut-off for different levels of significance using 1000 random datasets • - define significant signal range: max peak cut-off 0.005 aver signal CAAT-box

31. PEAKS Step 3. Build “promoter type” NFkB TATA Sp1 BACH1 52 genes regulated by NFkB, p < 0.5%

32. PEAKS server http://genomics.imim.es/peaks/ Bellora, Farré and Albà (2007). Bioinformatics 23, 243-4.

33. PEAKS results human promoter sequences NFkB TATA TATA YY GC-box GC-box CAAT BACH1 52 NFkB regulated genes 308 housekeeping genes TRANSFAC vertebrate matrices

34. PEAKS results promoters from yeast genes, amino acid metabolism (86 genes) - 54 yeast weight matrices tested - significant regions detected by the method show significant enrichment in experimentally-validated sites

35. promoter species 1 species 2 promoter species 1 species 2 Measuring promoter sequence divergence Divergence (Non-aligned promoter fraction or dSM) 1. highly divergent -> less constraints 0.8 2. highly conserved -> more constraints 0.4 Castillo-Davis et al., 2004

36. 0-0.1 0.1- 0.2- 0.3- 0.4- 0.5- 0.6- 0.7- 0.8- 0.9-1 1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Variability in promoter sequence divergence Average divergence = 70% 8385 human-mouse orthologues 2 Kb from transcription start site

37. Regulatory genes contain more conserved promoters than structural/metabolic genes Functional classes enriched in high score promoter alignments Lee et al. (2006). BMC Genomics 6: 188 - consistent with results by Iwama and Gojobori (2004)

38. Structural/metabolic genes contain less highly conserved promoters than regulatory genes Functional classes enriched in low score promoter alignments Lee et al. (2006). BMC Genomics 6: 188

39. Comparison neurogenesis versus ribosomal ribosomal neurogenesis Lee et al. (2006). BMC Genomics 6: 188

40. Is expression breadth related to promoter sequence divergence? orthologues human-mouse housekeeping tissue-specific intermediate Expression data from Zhang et al. (2004)

41. promoter species 1 species 2 Measure sequence divergence Divergence = non-aligned promoter fraction 2 Kb • tissue-specific • intermediate • housekeeping

42. Relationship between promoter divergence and expression breadth promoter divergence coding sequence divergence tissue-specific intermediate housekeeping Coding sequence evolutionary rate number of tissues but.. Promoter divergence

43. Relationship between promoter divergence and expression breadth % conservation TSS non-housekeeping housekeeping - divergence measured in 100 nt bins

44. Promoter divergence and gene function highly conserved promoter receptor binding signal transducer activity receptor activity structural molecule activity transcription regulator activity transcription factor activity DNA binding highly divergent promoter RNA binding ligase activity hydrolase activity catalytic activity GO class > 50 genes, p-value < 0.01

45. Promoter divergence and gene function divergence

46. Summary • the prediction of transcription factor binding sites • is very noisy, we need to use comparative genomics • some motifs show positional bias, this property can • help us understand the structure of promoters and improve motif predictions • promoter sequence conservation is related to gene function and to gene expression breadth. the fact that housekeeping genes contain less conserved promoters may obey to a more simple gene expression regulation

47. The team Evolutionary Genomics Group Universitat Pompeu Fabra, Barcelona http://genomics.imim.es/evolgenome Nicolas Bellora Domènec Farré Macarena Toll Medya Shikhagaie Loris Mularoni