Transcription Regulation and Gene Expression in Eukaryotes FS08 Pharmacenter/Biocenter Auditorium 1 Wednesdays 16h15-18h00.

Transcription

1 Transcription Regulation and Gene Expression in Eukaryotes FS08 Pharmacenter/Biocenter Auditorium 1 Wednesdays 16h15-18h00. Promoters and Enhancers Systematic discovery of transcriptional regulatory motifs by comparative genomics RG Clerc. February 20, 2008

2 The general problem RNA Transcription factor binding sites convey specificity to regulation

3 Functional classes of promoters Cramer et al., Science, 28 April RNA Polymerases: Pol I Pol II Pol III rrna mrna small RNAs 3 categories of promoters

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5 Definition of regulatory elements

6 Definition of regulatory elements

7 Binding Assay on Random DNA oligomers: PCR Binding Site Selection

8 Pol I promoters

9 Eukaryotic Pol II promoter: upstream and core NF-kB Fos Oct-1 CBFA-1 CREB etc Transcription activators TATA Basal machinery INR GENE RNA PolII 12 TFIIA 3 TFIIB 1 TFIID (TBP) ca. 14 (1) TFIIE 2 TFIIF 2 TFIIH ca. 9 upstream (regulatory, enhancer) core

10 Transcription factor binding sites convey specificity to promoters The TRANSFAC database contains ~600 redundant matrix description of known transcription factor binding sites (~350 transcription factors) Example: CREB (6 bases) average match 1 / 4>6 (4096 bp), 600,000 matches in the genome, 25,000 genes (Remedy: focus on evolutionary conserved binding sites!)

11 The core promoters in more detail BRE TATA box G/CG/CG/CCGCC TATAAA Initiator PyPyANT/APyPy DPE GA/TCGTG TFIIB TFIID TFIID TFIID TFIIA TBP TAF II 250 TAF II 150 TAF II 60 TAF II 40

12 The TATA box Matrices are derived from known binding sites or binding assays on random DNA oligomers

13 Definition of the upstream promoter elements Example: CAAT (4 bases) average match 1 / 4>4 (256 bp), 1,2 10>6 matches in the genome!

14 Transcriptional enhancers/silencers: First identified in viruses, then in cellular genes Activate (or repress) transcription independently of position (distance) and orientation (no polarity) Are made (and can be created) from partially redundant modules Confer cell-specific or temporal regulation No activity + Reporter Gene Low activity High activity

15 Identification of enhancer modules Enhancer activity is the result of interaction between individual modules

16 Pol III promoters

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19 Systematic discovery of transcriptional regulatory motifs by comparative genomics

20 From computer software to genomes - bioinformatics input int main (int argc, char **argv){int i, j, k;array seqs;linestream ls;int len;char *line;char *cp1, *cp2;int conscount;float compile consscore;seqs = arraycreate(50, char *);ls = ls_createfromfile (argv[1]); line = ls_nextline(ls);while (line = ls_nextline(ls)) {array(seqs, arraymax(seqs), char *) = hlr_strdup(line+10);} ls_destroy(ls);len = program execute strlen(arru(seqs, 0, char*));for (i=0; i<len; i++) {conscount = consscore = 0;for (j=0; j<arraymax(seqs)-1; j++) {cp1 = arru(seqs, j, char*);for (k=j+1; k<arraymax(seqs); k++) {cp = arru(seqs, k, char*);if (cp1 [i] == cp2 [i])consscore += 1;if (cp1 [i] == '-' && cp2 [i] == '-')consscore -= 1;consCount++;}}printf("%d\t%4.2f\n",i+1, output consscore/conscount); }exit(1);} transcrib e splice environment CACTGGCCAGAGACCCTTGCTCTGCTTAAGAGAATCAGGGCCCAACAAGTTTTACTCTTTGTTTCCTGCCTCCCAGGCTTAGTAAAATCACCAACT GACCATCCAAAGGAAGAGCCAGTTCATGCAGAGGCTGCACAGCTACTGGACACTGAAGCGGCAGTCACGGAATGGGGTCCCATTGCTACGTCGCCG translate ACACACCTGCAATCTCAGAGGAACTGTGACCAAGTTGGGGTACTGTGTCCAGTTCCCTGTGGGCTCTGGGAACTAGCGCCAGGGGGACGAAAACCT TTGCAACTGTAGTTTCCAGTGTCAGAGGGCTTAGACTCTTTCCTCCTTTAAGCTAGCCCTCAAACCAGGATCAGCATGGAATGTTCAATGAAAAGT genome catalyze GTTCCTATGTGTTTATGTATTCATTCAACAAACACATACCAAGCATTTATTCTGCCAGGCCTTGGGGACACACAGATTGAGGCACTACCAATAAAT GTGCTCCCTCTTTTGGCTTGACCTGAGTTTCCTGGGCTCTCTTCTCCCTGTTGACCTTTCCTCTGGCTTTCTAATCATCCTCTGATCTTCGTTTTC TACAGAGAGATTCTGAGGATAAGAACTGGGCCCTTAAAGAACAGCTCAAGTCCTGGCAGCGGCTCCGGCATGACTTGGAGCGAGCTCGGCTGCTCA ATTGATCCGCAAGCGGGAAAAACTCAAAAGGGAGACGGTGAGTGCTCCTGGGCCAGCCCTATTTTATAAAAGAAAAAACAAAAAATTAGCCAGGCT phenotype Genome-wide annotation of regulatory motifs using comparative genomics promoters, enhancers, etc

21 Genomes are not computer programs Program Genome sensitive to errors robust efficient, economical (if highly redundant good) Functional dynamically sequences in the human genome: Functional sequences in the static human genome: ~ undergoing 2% protein changes coding ~ 2% protein coding product of an evolution ~ 3% regulatory or structural has a purpose and is ~ 3% process regulatory or structural product of a design functional parts + process junk source code + programmer s comments

22 Neutral evolutionary rates differ within a genome

23 Conservation of functional sites: Mutations strike everywhere... but some are selected against gene conserved vs. non-conserved over the entire transcriptional unit

24 Pairwise sequence comparisons: the phylogenetic footprint concept (Duret et al 1997) Ureta-Vidal et al., Nat. Rev. Genetics, 2003

25 Pairwise comparison to mouse: Dermitzakis & Clark 2002 Based on on genes compared, % 40% of of known regulatory sites sites are are not not detected in in a a human-mouse pairwise comparison.

26 Pairwise comparison to mouse: Liu et al Based on on genes compared, 81% 81% of of known regulatory sites sites are are more more than than 50% 50% conserved in in a a human-mouse pairwise comparison.

27 Pairwise comparison to chicken: Thomas et al Based on on MB MB of of human genomic sequence, chicken detects 94% 94% of of the the coding exonsbut but only only 29% 29% of of the the non-coding conserved regions.

28 Pairwise sequence comparisons Ureta-Vidal et al., Nat. Rev. Genetics, 2003

29 More genomes = more problems high quality, finished assembly errors, sequence gaps Dog Cow Mouse Rat Oposs. GAPDH SLIT3 BACE1 fragments only

30 Comparative genomics database Assembled genome drafts from human, mouse, rat, and dog; genomic contigs from cow; opossum contigs under consideration 17,647 genomic loci from human genome analyzed (full loci) 11,716 loci are covered by human dog cow mouse rat 1,066,662 conservation peaks identified, of which 226,314 are accounted for by known coding exons

31 Comparative genomics: key learnings We detect transcription factor binding sites because conserved during evolution Check for promoter region only is bad! Check for full loci (entire trx unit) is highly recommended! Check for modules of several factor binding sites Do we detect transcription factor binding sites because they are conserved during evolution? Genomic sequences come in different qualities Need to mask problematic bad sequences, parts which do not make sense