1. http://cs173.stanford.edu [BejeranoWinter12/13] 1 MW 11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu CS173 Lecture 8: Transcriptional regulation II

2. http://cs173.stanford.edu [BejeranoWinter12/13] 2 Announcements HW1 due today. Thoughts and comments? HW2 will be out by midnight Halfway feedback today

3. http://cs173.stanford.edu [BejeranoWinter12/13] 3 Announcements

4. http://cs173.stanford.edu [BejeranoWinter12/13] 4 TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTT CTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGT TTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATAC CTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCT TGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTA AGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGA GTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACA GCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAAC CAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAA CACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTG GTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTC TCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAAT GCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCT TGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTT TCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCT ATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTT TCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGA GATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTA TCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTT CATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTT CAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAA TAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGT ATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG

5. Gene Regulation http://cs173.stanford.edu [BejeranoWinter12/13] 5 Some proteins and non coding RNAs go “back” to bind DNA near genes, turning these genes on and off. Gene DNA Proteins Gene activation:

6. http://cs173.stanford.edu [BejeranoWinter12/13] 6 Transcription Activation contd.

7. Transcription Activation http://cs173.stanford.edu [BejeranoWinter12/13] Terminology: RNA polymerase Transcription Factor Transcription Factor Binding Site Promoter Enhancer Gene Regulatory Domain 7 TF DNA

8. http://cs173.stanford.edu [BejeranoWinter12/13] 8 Transcription activation “loop” Gene Transcription factors bind DNA, turn on or off different promoters and enhancers, which in-turn turn on or off different genes, some of which may themselves be transcription factors, which again changes the presence of TFs in the cell, the state of active promoters/enhancers etc. Proteins DNA transcription factor binding site

9. IFN beta enhancer http://cs173.stanford.edu [BejeranoWinter12/13]9

10. Transcription Measurements http://cs173.stanford.edu [BejeranoWinter12/13] Some measurement techniques: Chromatin Immunoprecipitation Transcription output: –Transfection –Transgenics –Genome Engineering Chromosome Conformation Capture 10

11. Transcription Activation Properties http://cs173.stanford.edu [BejeranoWinter12/13] Observed Properties: Most TF binding site basepair preferences are independent of each other. TFs can synergize to turn gene activity on. Behavior can change in different conditions. TFs bind to hundreds and thousands of different targets in a single condition. Enhancers complement in different tissues. 11

12. Gene Regulation is HOT Gene regulation is currently one of the hottest topics in the study of the human genome. Large projects are pouring lots of money to generate large descriptive datasets. The challenge now is to glean logic from these piles. http://cs173.stanford.edu [BejeranoWinter12/13] 12 Measured >100 TFs in >70 cellular conditions. How does TF binding determine its output: gene expression?

13. System output measurements Measure non/coding gene expression! 1. First generation mRNA (cDNA) and EST sequencing: http://cs173.stanford.edu [BejeranoWinter12/13] 13 In UCSC Browser:

14. 2. Gene Expression Microarrays (“chips”) http://cs173.stanford.edu [BejeranoWinter12/13] 14

15. 3. RNA-seq “Next” (2nd) generation sequencing. http://cs173.stanford.edu [BejeranoWinter12/13] 15

16. http://cs173.stanford.edu [BejeranoWinter12/13] 16 Gene Finding II: technology dependence Challenge: “Find the genes, the whole genes, and nothing but the genes” We started out trying to predict genes directly from the genome. When you measure gene expression, the challenge changes: Now you want to build gene models from your observations. These are both technology dependent challenges. The hybrid: what we measure is a tiny fraction of the space-time state space for cells in our body. We want to generalize from measured states and improve our predictions for the full compendium of states.

17. 4. Spatial-temporal maps generation http://cs173.stanford.edu [BejeranoWinter12/13] 17

18. Gene Expression Causality Measuring gene expression over time provides sets of genes that change their expression in synchrony. But who regulates whom? Some of the necessary regulators may not change their expression level when measured, and yet be essential. “Reading” enhancers can provide gene regulatory logic: If present(TF A, TF B, TF C) then turn on nearby gene X http://cs173.stanford.edu [BejeranoWinter12/13] 18

19. Some Computational Challenges in Gene Regulation Transcription factor binding site discovery Technology-dependent challenge in constructing the correct binding site model (e.g. motif) from the measurements. Eg, ChIP produces sequences of 100-200bp. Your motif of length 4-20 is there somewhere. Find the most enriched model in the set of sequences you obtained. Methods range between full enumeration, heuristic/probabilistic searches, and hybrids. http://cs173.stanford.edu [BejeranoWinter12/13] 19

20. Transcription factor motif discovery: different technologies SELEX = Systematic Evolution of Ligands by Exponential Enrichment PBM = Protein Binding Microarrays http://cs173.stanford.edu [BejeranoWinter12/13] 20

21. Transcription factor binding site prediction Given the genome, and possibly some cell measurements predict (all and nothing but) the binding sites of a given transcription factor (in a/all context/s). http://cs173.stanford.edu [BejeranoWinter12/13] 21

22. Enhancer Prediction How do TFs “sum” together to provide the activity of an enhancer? A network of genes? http://cs173.stanford.edu [BejeranoWinter12/13] 22

23. Enhancer Prediction Given a sequence of DNA predict: Is it an enhancer? Ie, can it drive gene expression? If so, in which cells? At which times? Driven by which transcription factor binding sites? Given a set of different enhancers driving expression in the same population of cells: Do they share any logic? If so what is it? Can you generalize this logic to find new enhancers? http://cs173.stanford.edu [BejeranoWinter12/13] 23

24. Biology is empirical: you predict, and you measure! Measuring is great. It allows you to check your assumptions and improve your models until you get it. Some difficulties associated with gene regulation: Single cell measurements are rare. You most often measure some “average” over a population of cells. The population of cells is seldom in sync (same state). The closer a population of cells is to its in vivo state the less homogeneous it is. The closer a population of cells is to its in vivo state the harder (time, effort, money) it is to measure it. http://cs173.stanford.edu [BejeranoWinter12/13] 24

25. Biology is empirical, you predict, and you measure! Some more difficulties associated with gene regulation: A family of TFs often has very similar binding motifs Expression pattern may be different (but unknown to you). Family members may have different protein-protein interaction (PPI) domains which are also important. The genome is pleiotropic ( = good for all contexts). If an enhancer you are studying is in fact good for multiple contexts they will be overlaid on each other in sequence and make prediction (and disentanglement) harder. http://cs173.stanford.edu [BejeranoWinter12/13] 25

26. Transcription Factors Large “fan outs” revisited TFs reproducibly bind to thousands of genomic locations almost anywhere we’ve looked. Gene regulation forms a dense network. However, when such a TF is perturbed (over expressed or silenced) only a fraction of the genes it binds next to change their expression levels. http://cs173.stanford.edu [BejeranoWinter12/13] 26

27. Genomics vs. Genetics Last but not least – genomics is descriptive. It can show you “everything”. Eg: all the location a given transcription factor is bound to the genome (reproducibly) in a given cell state. Which of these bindings actually matters? http://cs173.stanford.edu [BejeranoWinter12/13] 27 frequency effect on cell adverse effect observable in experiments adverse effect on cell no or near no effect FunctionAssay Binds reproduciblyRelatively easy Changes expression of nearby genes Hard Affects cell/organism function/fitness Very hard Affects cell/organism but not where/when I looked for it (pleiotropy) Harder still

28. Transcription factors “rule” http://cs173.stanford.edu [BejeranoWinter12/13] 28 Cellular reprogramming is done by adding to the cell large quantities of a small number of the “right” TFs. These somehow “reset” cell state. We have learned (in a dish) to: 1 control differentiation 2 reverse differentiation 3 hop between different states

29. http://cs173.stanford.edu [BejeranoWinter12/13] 29 Transcription Regulation is not just about activation

30. Transcriptional Repression http://cs173.stanford.edu [BejeranoWinter12/13] An equally important but less visible part of transcription (tx) regulation is transcriptional repression (that lowers/ablates tx output). Transcription factors can bind key genomic sites, preventing/repelling the binding of –The RNA polymerase machinery –Activating transcription factors (including via competitive binding) Some transcription factors have stereotypical roles as activators or repressors. Likely many can do both (in different contexts). DNA can be bent into 3D shape preventing enhancer – promoter interactions. Activator and co-activator proteins can be modified into inactive states. Note: repressor thus can relate to specific DNA sequences or proteins. 30

31. Transcriptional Output Prediction http://cs173.stanford.edu [BejeranoWinter12/13] All these can increase or decrease tx output: Adding/repressing different proteins Modifying DNA bases Adding genomic context Changing cellular context Repression logic is harder to tease out. (need positive controls) 31

32. http://cs173.stanford.edu [BejeranoWinter12/13] 32 Transcription can only happen in open Chromatin Chromatin / Proteins DNA / Proteins Genome packaging in fact provides a critical layer of gene regulation.

33. Gene Activation / Repression via Chromatin Remodeling A dedicated machinery opens and closes chromatin. Interactions with this machinery turns genes and/or gene regulatory regions like enhancers and repressors on or off (by making the genomic DNA in/accessible) http://cs173.stanford.edu [BejeranoWinter12/13] 33

34. Insulators http://cs173.stanford.edu [BejeranoWinter12/13] Insulators are DNA sequences that when placed between target gene and enhancer prevent enhancer from acting on the gene. Known insulators contain binding sites for a specific DNA binding protein (CTCF) that is involved in DNA 3D conformation. However, CTCF fulfills additional roles besides insulation. I.e, the presence of a CTCF site does not ensure that a genomic region acts as an insulator. 34 TSS1 TSS2 Insulator

35. http://cs173.stanford.edu [BejeranoWinter12/13] 35 Cis-Regulatory Components Low level (“atoms”): Promoter motifs (TATA box, etc) Transcription factor binding sites (TFBS) Mid Level: Promoter Enhancers Repressors/silencers Insulators/boundary elements Cis-regulatory modules (CRM) Locus control regions (LCR) High Level: Epigenetic domains / signatures Gene expression domains Gene regulatory networks (GRN)

36. Signal Transduction Everything we discussed so far happens within the cell. But cells talk to each other, copiously. http://cs173.stanford.edu [BejeranoWinter12/13] 36

37. http://cs173.stanford.edu [BejeranoWinter12/13] 37 Gene Regulation II Chromatin / Proteins DNA / Proteins Extracellular signals To be continued…

38. http://cs173.stanford.edu [BejeranoWinter12/13] 38 (On Mondays) ask students to stack the chairs without wheels at the back of the room at the end of class.

39. http://cs173.stanford.edu [BejeranoWinter12/13] 39