L19 Gene Regulation

BMMB 554 L19 Gene Regulation

Transcript

1. L19: GENE REGULATION Foundations in Data Driven Life Sciences BMMB/MCIBS 554
2. Today’s learning objectives • Understand how transcription factors are important for gene regulation. • Learn how to represent the binding preference of a transcription factor. • Learn how to find potential binding sites in DNA sequences.
3. WHY STUDY PROTEIN-DNA BINDING?
4. Regulation of genes/proteins can be affected by many mechanisms • Chromatin structure • Epigenetic modifications / histone marks • General transcriptional machinery • Sequence-specific transcription factors • Activators & repressors • Non-coding RNA activity • Post-transcriptional regulators • Alternate splicing • miRNAs, siRNAs, etc. • Post-translational modifications • Protein degradation
5. Regulation of genes/proteins can be affected by many mechanisms • Chromatin structure / nucleosome positions • Epigenetic modifications / histone marks • General transcriptional machinery • Sequence-specific transcription factors • Activators & repressors • Non-coding RNA activity • Post-transcriptional regulators • Alternate splicing • miRNAs, siRNAs, etc. • Post-translational modifications • Protein degradation Assayable by ChIP-seq
6. TFs can promote transcriptional initiation Distal enhancers can promote pre- initiation complex formation and transcriptional initiation via mediator Molecular Biology of the Cell (Garland Science)
7. TFs can promote transcriptional elongation Promoter-proximal pausing occurs at most Pol II genes. Activators can promote the release of the paused polymerase to enable active elongation. Adelman & Lis, Nature Reviews Genetics (2012)
8. TFs promote chromatin remodeling Chromatin remodeling complexes: • Histone acetyltransferases (HATs) / deacetylases • Methyltransferases / demethylases • ATP-dependent chromatin remodelers • Mediator, cohesin • GTFs, Pol II Molecular Biology of the Cell (Garland Science)
9. TFs can repress transcription Molecular Biology of the Cell (Garland Science)
10. Transcription factors are key to cellular identity pluripotent fibroblast motor neuron dopa+ neuron cardiomyocyte cortical neuron Ascl1, Brn2, Myt1l Ngn2, Isl1, Lhx3 Ascl1, Nurr1, Lmx1a Ascl1, Brn2, Myt1l Gata4, Mef2c, Tbx5 Ngn3, Pdx1, Mafa exocrine β-cell myoblast MyoD Oct4, Sox2, Klf4, c-Myc
11. Transcription factors are key to cellular identity pluripotent fibroblast motor neuron dopa+ neuron cardiomyocyte cortical neuron Ascl1, Brn2, Myt1l Ngn2, Isl1, Lhx3 Ascl1, Nurr1, Lmx1a Ascl1, Brn2, Myt1l Gata4, Mef2c, Tbx5 Ngn3, Pdx1, Mafa exocrine β-cell myoblast MyoD Oct4, Sox2, Klf4, c-Myc oncogenesis oncogenes (myc) tumor suppressors (p53)
12. HOW DO WE FIND POTENTIAL TF BINDING SITES?
13. CCGGAA TGACCT..TGACCT ATTA CACGTG Transcription factor binding sites • Short: Typically between 6 – 20bp long • Degenerate: TFs have favorite binding sequences but don’t require a perfect match to bind. Likes to bind:
14. We can represent a TF’s binding preference as a multiple alignment GGGAATTTCC GGGAATTTCC GGGAATTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGAAATTCC GGGACTTCCC GGGGATTTCC GGGGATTTCC GGGGATTTCC GGGATTTTCC GGGGATTCCC GGGATTTCCC GGGAATTCAC GGGGCTTTCC GGGGCTTTCC GGGAAGTCCC TF: NFκB Favorite sequence: GGGAATTTCC Known NFκB binding sites: Consensus sequence Weight matrix GGGRNWTYCC Definition: A motif is a pattern of conservation in a sequence alignment.
15. Position weight matrices Definition: A position weight matrix (PWM) is a matrix of log likelihood values that gives a weighted match to fixed length strings. Also known as position specific scoring matrix (PSSM). GGGAATTTCC GGGAATTTCC GGGAATTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGAAATTCC GGGACTTCCC GGGGATTTCC GGGGATTTCC GGGGATTTCC GGGATTTTCC GGGGATTCCC GGGATTTCCC GGGAATTCAC GGGGCTTTCC 1 2 3 4 5 6 7 8 9 10 A 0 0 0 15 9 1 0 0 1 0 C 0 0 0 0 9 0 0 4 19 20 G 20 20 20 5 0 0 0 0 0 0 T 0 0 0 0 2 19 22 16 0 0 1 2 3 4 5 6 7 8 9 10 A 0.00 0.00 0.00 0.75 0.45 0.05 0.00 0.00 0.05 0.00 C 0.00 0.00 0.00 0.00 0.45 0.00 0.00 0.20 0.95 1.00 G 1.00 1.00 1.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00 T 0.00 0.00 0.00 0.00 0.10 0.95 1.00 0.80 0.00 0.00 Count matrix Relative frequency matrix pi,j : probability of observing letter i at position j
16. Motif logos Sequence logo IC j = 2 −(− p i, j log 2 (p i, j ) i=A T ∑ ) Height i, j = p i, j IC j Information content: Letter height in logo: 1 2 3 4 5 6 7 8 9 10 A 0.00 0.00 0.00 0.75 0.45 0.05 0.00 0.00 0.05 0.00 C 0.00 0.00 0.00 0.00 0.45 0.00 0.00 0.20 0.95 1.00 G 1.00 1.00 1.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00 T 0.00 0.00 0.00 0.00 0.10 0.95 1.00 0.80 0.00 0.00 Relative frequency matrix pi,j : probability of observing letter i at position j
17. Position weight matrices 1 2 3 4 5 6 7 8 9 10 A -4.64 -4.64 -4.64 1.55 0.82 -2.64 -4.64 -4.64 -2.64 -4.64 C -4.64 -4.64 -4.64 -4.64 0.82 -4.64 -4.64 -0.40 1.91 1.96 G 1.96 1.96 1.96 0.00 -4.64 -4.64 -4.64 -4.64 -4.64 -4.64 T -4.64 -4.64 -4.64 -4.64 -1.06 1.91 1.96 1.66 -4.64 -4.64 Position weight matrix: mi,j = log2 (pi,j / bi ) where bi is the background probability of letter i 1 2 3 4 5 6 7 8 9 10 A 0.01 0.01 0.01 0.73 0.44 0.04 0.01 0.01 0.04 0.01 C 0.01 0.01 0.01 0.01 0.44 0.01 0.01 0.19 0.94 0.97 G 0.97 0.97 0.97 0.25 0.01 0.01 0.01 0.01 0.01 0.01 T 0.01 0.01 0.01 0.01 0.12 0.94 0.97 0.79 0.01 0.01 Relative frequency matrix pi,j : probability of observing letter i at position j A 0.25 C 0.25 G 0.25 T 0.25 Background frequencies bi : probability of observing letter i ÷
18. Motif scanning: finding instances of a known motif 1 2 3 4 5 6 7 8 9 10 A -4.70 -4.70 -4.70 1.55 0.82 -2.12 -4.70 -4.70 -2.12 -4.70 C -4.70 -4.70 -4.70 -4.70 0.82 -4.70 -4.70 -0.31 1.88 1.96 G 1.96 1.96 1.96 0.00 -4.70 -4.70 -4.70 -4.70 -4.70 -4.70 T -4.70 -4.70 -4.70 -4.70 -1.24 1.88 1.96 1.64 -4.70 -4.70 >Test Sequence TTACGTTTGTCGATTTATGGGACTTTCCTCTTCGTATTTATTAGGCT 1 2 3 4 5 6 7 8 9 10 A -4.70 -4.70 -4.70 1.55 0.82 -2.12 -4.70 -4.70 -2.12 -4.70 C -4.70 -4.70 -4.70 -4.70 0.82 -4.70 -4.70 -0.31 1.88 1.96 G 1.96 1.96 1.96 0.00 -4.70 -4.70 -4.70 -4.70 -4.70 -4.70 T -4.70 -4.70 -4.70 -4.70 -1.24 1.88 1.96 1.64 -4.70 -4.70 T T A C G T T T G T C G A … A T G G G A C T T T C C T C Score = -27.42 Score = 17.57
19. Motif scanning tools • FIMO: http://meme-suite.org/tools/fimo • Part of the MEME suite of motif analysis tools. • MotifViz: http://biowulf.bu.edu/MotifViz/ • Various motif scanning tools included. • MATCH: Kel, et al. Nucleic Acids Res (2003) • TAMO: Gordon, et al. Bioinformatics (2005) • MotifScanner/TOUCAN: Aerts, et al. Nucleic Acids Res (2005)
20. FIMO: Finding Individual Motif Occurrences http://meme-suite.org/tools/fimo
21. FIMO: sample output
22. Databases of known motifs • Jaspar: • http://jaspar.genereg.net • Open access. • High quality motifs, medium coverage. • cis-bp: • http://cisbp.ccbr.utoronto.ca • Mostly based on in vitro protein binding microarray experiments. • Comprehensive: known or predicted motifs for most human TFs.
23. Finding known TF binding motifs (cis-bp)
24. HOW DO TFS FIND THEIR BINDING SITES?
25. How do transcription factors recognize their binding sites? C T A A T T A C T A A T T A T T A A G T A C T A A T G G T T A A T T G T T A A G G A C T A A T G A G T A A T T G Binding sites Sequence that best matches motif. 206,801 exact matches in mouse genome. 2,807 of these are bound by Isl1 in motor neurons. In general (vertebrate genomes): • Millions of motif instances. • Tens of thousands of binding sites. Isl1
26. 10Kbp Sequence preference alone does not fully predict TF binding in vivo
27. 10Kbp Sequence preference alone does not fully predict TF binding in vivo
28. 100Kbp Sequence preference alone does not fully predict TF binding in vivo
29. Most TF binding motif instances are unbound • Essentially all TF binding motif occurrences will have no function in a given cell type*. • How can we focus on motif instances that are more likely to be bound & functional? • Conservation • Cis-regulatory modules (i.e. clusters of sites) • Measuring TF binding (ChIP-seq) • Accessibility (DNaseI hypersensitivity) • Chromatin marks (H3K4me1, H3K27ac) * Wasserman & Sandelin, Nature Reviews Genetics (2004) Cell type dependent: we would need experimental data
30. Phylogenetic footprinting • Conserved sequences are more likely to be functional.
31. Limitations of phylogenetic footprinting • Only ~60% of TF binding sites are directly conserved between human & mouse. • Perhaps even fewer TF binding sites are functionally conserved. • Phylogenetic shadowing approaches allows conservation analysis of binding sites across more closely related species.
32. Cis-regulatory modules • Idea: motif instances should be clustered at real enhancers. • Different combinations of TFs may have different regulatory effects. Regulatory synergy
33. Regulatory regions consist of cooperatively bound TFs
34. Cis-regulatory modules • Multiple TF binding sites will form a stronger binding region. • Motif instances for cooperating TFs should be located together.
35. How do transcription factors recognize their regulatory targets in a given cell type? CHROMATIN STATE DEPENDENT ACCESSIBILITY TF XYZ COOPERATIVE INTERACTIONS TF XYZ PIONEER BINDING TF BEFORE AFTER BEFORE AFTER BEFORE AFTER NO DEPENDENCE ON PRIOR STATE…
36. Chromatin structure may determine TF binding locations • Regulatory sites are typically located in regions of accessible chromatin. • Enhancers and promoters have characteristic histone modifications. • H3K4me1 at enhancers • H3K4me3 at promoters • TFs may interact differently with methylated DNA. • Higher-order genome topology may also play a role in TF binding. Rosa & Shaw, Biology (2013)
37. PROTEIN-DNA BINDING ASSAYS
38. Chromatin immunoprecipitation (ChIP) 1. Crosslink: Use formaldehyde or UV light to covalently crosslink proteins to DNA (i.e. stick everything together) 2. Lyse: Use a lysis buffer to break down cell walls and release chromatin. 3. Shear: Use sonication or nuclease digestion to break the crosslinked chromatin into small fragments.
39. Chromatin immunoprecipitation (ChIP) 4. Immunoprecipitate: Use antibodies attached to beads to select for DNA fragments that have a protein of interest attached. Separate out beads using magnet for magnetic beads or centrifugation for agarose beads. Y Y Y Y Y magnetic bead with attached antibodies Y
40. Chromatin immunoprecipitation (ChIP) 5. Reverse crosslinks Incubate at 70°C to reverse cross-linking (i.e. unstick DNA from protein), and separate DNA.
41. Chromatin immunoprecipitation (ChIP) 6. Library preparation: Select DNA fragments that are ~200bp in length. Amplify the fragments using PCR. Size select PCR amplify
42. ChIP-seq • Directly sequence ChIPed DNA fragments using a high-throughput sequencer. • Sequence one or both fragment ends? • à FASTQ file • Map the sequenced reads back to the genome. • Bowtie / BWA • à BAM file
43. ChIP-exo provides higher resolution
44. Other protein-DNA binding assays
45. Using MNase to find protein-DNA interactions
46. Summary • We cannot (yet) predict where a given transcription factor will bind in a given cell type. • Motif scanning yields too many potential sites. • ChIP-seq or other experimental approaches are required.