1. Detecting binding sites for transcription factors by correlating sequence data with expression. Erik Aurell Adam Ameur Jakub Orzechowski Westholm in collaboration with AstraZeneca

2. Outline of the talk Biological background: Protein synthesis Gene expression Gene regulation The REDUCE method: Introduction Data description Methods Results Applications and Conclusions Ameur, Orzechowski 18/3 2003

3. Protein synthesis Ameur, Orzechowski 18/3 2003

4. Gene expression Expression levels are controlled by a gene regulation mechanism. All cells in an organism contain the same DNA, but they still have different properties: blood cells, skin cells, liver cells, etc. The properties are determined by the protein concentrations in the cell. The amount of protein produced from a gene is called the expression level. Thus, the expression level for a gene differ between cells. Expression can also differ in the same cell over time. Ameur, Orzechowski 18/3 2003

5. Gene regulation Control of transcription is the most important form of gene regulation. When a gene is transcribed a transcriptase binds to the promoter region. Other proteins, transcription factors, can also bind to the genome close to the promoter region. The transcription factors can either attract or repel the transcriptase. Combinatorial control: TFs can work together to control the level of transcription. Ameur, Orzechowski 18/3 2003

6. Regulatory networks Since TFs are proteins, they are products of genes. This implies that genes regulate the expressions of other genes, forming a regulatory network. Understanding how the regulatory networks function is a big challenge in biology. Ameur, Orzechowski 18/3 2003

7. Introduction - the REDUCE method The aim is to find binding sites for transcription factors, motifs, in the human genome by using a method developed at Rockefeller University (Bussemaker, Li & Siggia 2001). This method is called REDUCE and has previously only been applied to yeast data. We will apply it to human data. The idea is to find motifs by correlating sequence and expression data. Input consists of: Expression data, sequence data and a set of putative motifs. Output is a list of significant motifs: consensus id description   F probes hits NNNRRCCAATSRGNNN M00287 NF-Y 0.0044 0.0661 1041 1279 NNNCGGCCATCTTGNCTSNW M00069 YY1 0.0014 0.0363 300 314 NNRACAGGTGYAN M00060 Sn 0.0013 -0.0345 368 374 NNNRGGNCAAAGKTCANNN M00134 HNF-4 0.0008 0.0290 263 272 TWTTTAATTGGTT M00424 NKX6-1 0.0007 -0.0234 428 457 KNNKNNTYGCGTGCMS M00235 AhR/Arnt 0.0006 -0.0254 155 161 NANCACGTGNNW M00123 c-Myc/Max 0.0006 -0.0243 50 50 NNBTNTNCTATTTNTT M00092 BR-CZ2 0.0005 0.0233 92 94 NNGAATATKCANNNN M00136 Oct-1 0.0005 -0.0230 213 244 Ameur, Orzechowski 18/3 2003

8. Expression data Expression data is provided by AstraZeneca. It consists of 81 samples of human cerebral cortex stem cells undergoing various treatments. Expressions are measured on an Affymetrix u133 chip. We visualize expression data in a heatmap. It is possible to identify regions of correlated genes in the heatmap. Ameur, Orzechowski 18/3 2003

9. Sequence data In the REDUCE model, expression levels are explained by the number of times the motifs occur in the upstream sequences of human genes. For this, sequences around the transcription starts are extracted. We take sequences in the range [1000 bp upstream, 100 bp downstream]. Transcription starts and genome data are provided by AstraZeneca. The upstream sequences are masked for repeats (with the program RepeatMasker). Putative motifs are matched to the resulting sequences. The motif TKAAA and its reverse complement TTTMA are matched in the example. Ameur, Orzechowski 18/3 2003

10. Motifs Motifs are represented as weight matrices : We generate the set of putative motifs as weight matrices. This can be done in several ways: One possibility is to use the matrices (about 300) in the TransFac data base. Another possibility is to generate matrices of our own, for example for all sequences of a certain length. Since the number of possible sequences grows exponentially with the length, this is only possible for sequneces up to length 7 or 8. We have implemented a method based on Gibbs sampling to match weight matrices to upstream regions. w(i,B) is the probability that base i is the nucleotide B in the motif M. Ameur, Orzechowski 18/3 2003

11. Matching motifs to the upstream sequences A weight matrix W is matched to a sequence s 1 s 2 … s n the following way: For each of the bases s 1 s 2 … s n we extract the corresponding weight matrix entry w(i,s i ) and compute the following sum Here b s i is the background frequence of base s i. An example: Assume we have the sequence AATCG and the matrix If all background frequencies are 0.25, this would give the score The score is then compared to a threshold value: Ameur, Orzechowski 18/3 2003

12. Pre-processing and REDUCE Ameur, Orzechowski 18/3 2003

13. REDUCE iterations Let K be the set of putative motifs, M the motifs in the model, and G the set of all genes. The error of the model is defined as     G ( A g - (C+  M F  n  g )   Let M’ be the model M U  ’   hen we can define a score for each  ’,    ‘       ’, that tells how good the motif  ’ fits the model M The REDUCE method: M = empty K = {all putative motifs} while K is not empty Compute    for every  in K Let  ’ be the motif that gives the best fit (highest   value) M = M U  ’  K = K - {  ’} Remove all motifs in K with low   values Ameur, Orzechowski 18/3 2003

14. REDUCE output consensus id description   Fprobes hits NNNRRCCAATSRGNNN M00287 NF-Y 0.0044 0.0661 1041 1279 NNNCGGCCATCTTGNCTSNW M00069 YY1 0.0014 0.0363 300 314 NNRACAGGTGYAN M00060 Sn 0.0013 -0.0345 368 374 NNNRGGNCAAAGKTCANNN M00134 HNF-4 0.0008 0.0290 263 272 TWTTTAATTGGTT M00424 NKX6-1 0.0007 -0.0234 428 457 KNNKNNTYGCGTGCMS M00235 AhR/Arnt 0.0006 -0.0254 155 161 NANCACGTGNNW M00123 c-Myc/Max 0.0006 -0.0243 50 50 NNBTNTNCTATTTNTT M00092 BR-CZ2 0.0005 0.0233 92 94 NNGAATATKCANNNN M00136 Oct-1 0.0005 -0.0230 213 244 consensus - A consensus sequence for the motif. id - A unique id for each motif. description - The transcription factor name.   - The significance of the motif. F - The effect. A positive value indicates activation and negative repression. probes - Number of probes with occurences of the motif in their upstream regions. hits - Total number of motif occurences. Ameur, Orzechowski 18/3 2003

15. REDUCE outadata can be visualized in a heatmap. Visualizing REDUCE outdata The motifs in this heatmap are taken from TransFac. Green dots indicate repressing and red dots indicate activating motifs. The heatmap gives a clustering of samples on motifs. Ameur, Orzechowski 18/3 2003

16. Analyzing REDUCE outdata Validation: The pictures below show the samples clustered on expression and on motifs. Analysis of significant motifs: By analyzing the motifs found by REDUCE we hope to find motifs that explain clusters of correlated genes. For example, REDUCE found a TransFac motif in the samples associated with the red area in the picture. It matches 18% of the 109 genes in the picture, and 4% of the other genes. Finding new motifs:One iteration of REDUCE was run on all sequences of length 5. Ameur, Orzechowski 18/3 2003

17. Applications Identify coregulated genes with potentially different expression profiles, using the motifs found by REDUCE. Predict previously unknown motifs, or new properties of known ones. Conclusions Our results on human data had somewhat lower significance than previous results on yeast presented in (Bussemaker, Li & Siggia, 2001). There are several possible causes for this: Data quality: Expression data, upstream regions. Hard to validate findings. Gene regulation probably more complicated in human. Even so, our results suggest that the REDUCE method might give useful information about transcription factor binding sites in humans. Probably, this requires prior knowledge about motifs and other methods such as clustering. Ameur, Orzechowski 18/3 2003