1. Regulatory Motif Finding
Wenxiu Ma
CS374 Presentation
11/03/2005

2. Outline Regulation of genes Regulatory Motifs Motif Representation
Current Motif Discovery Methods

3. Regulation of Genes What turns genes on (producing a protein) and off?
When is a gene turned on or off?
Where (in which cells) is a gene turned on?
How many copies of the gene product are produced?
3 billions nucleotides of human genome.
Different cell types differ dramatically in both structure and functions.
Cell differentiation is often irreversible.
Q: In what condition is each gene product made, and once made, what does it do?

4. Overview of Gene Control
The mechanisms that control the expression of genes operate at many levels.
Gene expression can be regulated at many of the steps in the pathway from DNA to RNA to Protein.
Thus a cell can control the proteins it makes by
(1) controlling when and how often a given gene is transcribed (transcriptional control),
(2) controlling how the RNA transcript is spliced or otherwise processed (RNA processing control),
(3) selecting which completed mRNAs in the cell nucleus are exported to the cytosol and determining where in the cytosol they are localized (RNA transport and localization control),
(4) selecting which mRNAs in the cytoplasm are translated by ribosomes (translational control),
(5) selectively destabilizing certain mRNA molecules in the cytoplasm (mRNA degradation control), or
(6) selectively activating, inactivating, degrading, or compartmentalizing specific protein molecules after they have been made (protein activity control)
source: Molecular Biology of the Cell (4th ed.), A. Johnson, et al.

5. Transcriptional Regulation
The transcription of each gene is controlled by a regulatory region of DNA relatively near the transcription start site (TSS).
two types of fundamental components
short DNA regulatory elements
gene regulatory proteins that recognize and bind to them.

6. Regulation of Genes Transcription Factor (Protein) RNA polymerase
DNA
Gene
Regulatory Element
source: M. Tompa, U. of Washington

7. Regulation of Genes Transcription Factor (Protein) RNA polymerase DNA
Regulatory Element
Gene
source: M. Tompa, U. of Washington

8. Regulation of Genes New protein RNA polymerase Transcription Factor
DNA
Regulatory Element
Gene
source: M. Tompa, U. of Washington

9. Outline Regulation of genes Regulatory Motifs Motif Representation
Current Motif Discovery Methods

10. What is a motif?
A subsequence (substring) that occurs in multiple sequences with a biological importance.
Motifs can be totally constant or have variable elements.
Protein Motifs often result from structural features.
DNA Motifs (regulatory elements)
Binding sites for proteins
Short sequences (5-25)
Up to 1000 bp (or farther) from gene
Inexactly repeating patterns

11. daf-19 Binding Sites in C. elegans
GTTGTCATGGTGAC
GTTTCCATGGAAAC
GCTACCATGGCAAC
GTTACCATAGTAAC
GTTTCCATGGTAAC
che-2
daf-19
osm-1
osm-6
F02D8.3
-150 -1
source: Peter Swoboda

12. Motif Representing
Consensus sequence: a single string with the most likely sequence(+/- wildcards)
Regular expression: a string with wildcards, constrained selection
Profile: a list of the letter frequencies at each position
Sequence Logo:
graphical depiction of a profile
conservation of elements in a motif.

13. Motif Logos: an Example(

14. Measure of Conservation
Relative heights of letters reflect their abundance in the alignment.
Total height = entropy-based measurement of conservation.
Entropy(i) =
-SUM { f(base, i)* ln[f(base, i)] }
over all bases
Conservation(i) = 2- Entropy(i)
Units of conservation = bits of information
Entropy measures variability/disorder.
High conserved = low entropy = tall stack
Very variable = high entropy = low stack

15. Outline Regulation of genes Regulatory Motifs Motif Representation
Current Motif Discovery Methods

16. Finding Regulatory Motifs.
Given a collection of genes with common expression,
Find the (TF-binding) motif in common

17. Identifying Motifs: Complications
We do not know the motif sequence
We do not know where it is located relative to the genes start
Motifs can differ slightly from one gene to another
How to discern it from “random” motifs?

18. Current Motif Discovery Methods
GOAL: comprehensive identification of all the regulatory motifs in genomes.
by overrepresentation
MEME, Gibbs sampling
by phylogenetic footprinting
Footprinter
Cross species comparative analysis
Combine structure information
Majority of non-coding functional elements remain unknown.

19. Motif Finding: Comparative Analysis
Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals.
Xie, X. et al., Nature (2005).
Identify motifs based on comparative analysis of human, mouse, rat and dog genomes
A systematic catalogue of human gene regulatory motifs
Short, functional sequences (6-10bp) used many times in a genome
Focus regions
Promoters
3’ untranslated regions (3’ UTRs)
microRNAs (miRNAs)
post-transcriptional regulation
Comparative analysis take the advantage of the evolutionary conservation across related species.

20. Motif Discovery Procedure
Alignment of promoters & 3’ UTRs
Motif conservation score (MCS)
Measure the extent of excess conservation
“Highly conserved motifs”
MCS>6
Clustering

21. Alignment of promoters & 3’ UTRs
construct a whole-genome alignment for the four mammalian genomes
Blastz1 and Multiz2
Extract the aligned promoter and 3’ UTRs portions respectively.
Coordinates: the annotation of NCBI reference sequences (RefSeq)
constructed a whole-genome alignment for the four mammalian genomes using the program Blastz1 and Multiz2 in two steps: we first aligned human and dog sequences based on the human/dog syntenic map we generated (to be reported elsewhere, see Lindblad-Toh et al3). We then aligned the human/dog sequences to human/mouse/rat three-way alignment.

22. Motif Conservation Score (MCS)
Consensus sequence representation
Alphabet size: 11
(A,C,G,T,[AC], [AG], [AT], [CG], [CT], [GT], [ACGT])
conserved occurrence of a motif m is an instance in which an exact match to this motif is found in all four species.
conservation rate p = ratio of conserved occurrences to total occurrences in human
Expected conservation rate p0 =
avg. conservation rate of 100 random motifs,
given same length and redundancy.

23. MCS
MCS = # of s.d. by which the observed conservation rate of a motif p exceeds the expected conservation rate p0.
p = k/n
Binomial probability of observing k out of n
Estimated by way of Normal approximation to the binomial Dist.

24. Conservation Properties of Regulatory Motifs
Known 8-mer TGACCTTG
Conservation rate 37% (162 out of 434)
random rate 6.8%
MCS = 25.2 s.d.
Promoter Region
TRANSFAC: 446 motifs
MCS>3: 63%
MCS>5: ~50%
3’ UTR
no database analogous to TRANSFAC
some known motifs

25. Motif Discovery Procedure
Alignment of promoters & 3’ UTRs
Motif conservation score (MCS)
“Highly conserved motifs”
MCS>6
Clustering

26. Results: motifs in promoters
174 highly conserved motifs
59 strong match to known motifs, 10 weaker match.
105 potential new regulatory motifs
Tissue specificity and position bias
89% of known motifs
69% of new motifs
Xie, X. et al., Nature, 2005

27. Results: motifs in 3’ UTRs
106 highly conserved motifs
Two unusual properties
Strand specificity
Unusual length distribution

28. Property1: strand specificity
Xie, X. et al., Nature, 2005

29. Property2
Xie, X. et al., Nature, 2005

30. Properties => miRNA
Strand specificity
3’-UTR motifs acting at the level of RNA rather than DNA
have a role in post-transcriptional regulation
Length distribution
Many mature miRNA start with U followed by a 7-base “seed” complementary to a site in the 3’ UTR of target mRNAs.
Hypothesis: many of the highly conserved 8-mer motifs might be binding sites for conserved miRNAs.

31. The microRNA pathway Adapted from Tomari & Zamore Curr Biol 2004
7mG(5’)ppp(5’)G
The microRNA pathway
pri-miRNA
Drosha
Pasha
3’-nA…AAA
pre-miRNA
Dicer
miR/miR* duplex
mature miRNA
miRNP
Adapted from Tomari & Zamore Curr Biol 2004

32. Relationship with miRNA
72 highly conserved 8-mer motifs
Contiguous, non-degenerate
~46% of all 3’-UTR motifs
207 distinct human miRNAs
From current registry
Complementary matches
Exactly match: ~43.5%
One mismatch: ~50%
95% of matches begin at NT 1 or 2 of the miRNA gene
8-mer motifs represent target sites for miRNA

33. 8-mer motifs ->new miRNA genes
RNAfold program
242 conserved and stable stem-loop sequences
113 known, 129 potential new miRNAs
Biological validation
12 selected new miRNA genes
6 (50%) have clearly expression activity in tissues.

34. Prevalence of miRNA regulation
20% of 3’ UTRs may be targets for conserved miRNA-based regulation at the 8-mer motifs.
Unbiased assessment of the relative importance of miRNA-based regulation in the human genome

35. Summary: comparative genome analysis
4 mammalian species
an initial systematic catalogue
Promoters
3’ UTRs
Importance of the new miRNA regulatory mechanism
Future directions:
genome-wide discovery
more genomes alignments: the primate

36. Now… Motif Finding Methods Cross species comparative analysis
Combine structure information

37. Motif Finding: Structural Knowledge
Ab initio prediction of transcription factor targets using structural knowledge,
Kaplan T, et al., PLoS Comput Biol (2005)
Propose a general framework for predicting DNA BS sequences of novel TFs from known family
Structure-based approach
No prior TF binding data and target gene
Family-wise probabilistic model
Context-specific amino acid-nucleotide
recognition preferences
ab initio gene prediction: Traditionally, gene prediction programs that rely only on the statistical qualities of exons have been referred to as performing ab initio predictions. Ab initio prediction of coding sequences is an undeniable success by the standards of the machine- learning algorithm field, and most of the widely used gene prediction programs belong to this class of  algorithms.

38. Structure-based approach
Family-wise probabilistic model
Input:
pairs of TFs and their target DNA sequences
structural information
Output: Context-specific amino acid-nucleotide recognition preferences
Position specificity
Then, discover TFBSs of other TFs from the same family
In previous studies, we used the empirical frequencies of amino acid–nucleotide interactions [4,5] in solved complexes (from various protein families) to build a set of “DNA-recognition preferences.” This approach assumed similar DNA-binding preferences of the amino acids for all structural domains and at all binding positions. However, there are clear experimental indications that this assumption is not always valid: a particular amino acid may have different binding preferences depending on its positional context

39. Cys2His2 Zinc Finger protein family
largest known DNA-binding family in multicellular organisms
common, strict binding models
Figure One type of zinc finger protein. This protein belongs to the Cys-Cys-His-His family of zinc finger proteins, named after the amino acids that grasp the zinc. (A) Schematic drawing of the amino acid sequence of a zinc finger from a frog protein of this class. (B) The three-dimensional structure of this type of zinc finger is constructed from an antiparallel b sheet (amino acids 1 to 10) followed by an a helix (amino acids 12 to 24). The four amino acids that bind the zinc (Cys 3, Cys 6, His 19, and His 23) hold one end of the a helix firmly to one end of the b sheet. (Adapted from M.S. Lee et al., Science 245: , 1989.)
There Are Several Types of DNA-binding Zinc Finger Motifs
The helix-turn-helix motif is composed solely of amino acids. A second important group of DNA-binding motifs adds one or more zinc atoms as structural components. Although all such zinc-coordinated DNA-binding motifs are called zinc fingers, this description refers only to their appearance in schematic drawings dating from their initial discovery (Figure 7-17A). Subsequent structural studies have shown that they fall into several distinct structural groups, two of which are considered here. The first type was initially discovered in the protein that activates the transcription of a eucaryotic ribosomal RNA gene. It is a simple structure, consisting of an a helix and a b sheet held together by the zinc (Figure 7-17B).
source: Molecular Biology of the Cell (4th ed.), A. Johnson, et al.

40. Cys2His2 Zinc Finger: Canonical DNA binding model
Residues at positions 6, 3, 2, and -1 (relative to the beginning of the a-helix) at each finger interact with adjacent nucleotides in the DNA molecule (interactions shown with arrows).
Kaplan. et al., PLoS Comput Biol, 2005

41. Cys2His2 Zinc Finger: DNA Binding Model
Figure DNA binding by a zinc finger protein. (A) The structure of a fragment of a mouse gene regulatory protein bound to a specific DNA site. This protein recognizes DNA using three zinc fingers of the Cys-Cys-His-His type (see Figure 7-17) arranged as direct repeats. (B) The three fingers have similar amino acid sequences and contact the DNA in similar ways. In both (A) and (B) the zinc atom in each finger is represented by a small sphere. (Adapted from N. Pavletich and C. Pabo, Science 252: , 1991.)
source: Molecular Biology of the Cell (4th ed.), A. Johnson, et al.

42. Cys2His2 Zinc Finger: Compiling dataset
Goal: DNA-recognition preferences for each of the four key positions
every AA v.s. every NT
insufficient solved protein-DNA complex
Known protein sequence data and their DNA targets
TRANSFAC: 455 protein-DNA Pairs
Non-canonical model
Profile HMM
No exact binding locations
CX(2-4)CX(11-13)HX(3-5)H

43. Profile HMM “Silent” deletion states Insertion states Match states
build a model representing the consensus sequence for a family, rather than the sequence of any particular member
Find potential alignment for new sequences

44. Example: full profile HMM

45. Structure-based approach
Input: set of pairs of TFs and their target DNA sequences
Output: Context-specific amino acid-nucleotide recognition preferences
Iterative Expectation Maximization (EM) algorithm

46. Cys2His2 Zinc Finger: Probabilistic Model
The set of interacting residues in 4 different positions of the k fingers
N1,… NL be a target DNA sequence
The probability that an interaction starting from jth position in the DNA
where PP(N|A) is the conditional probability of nucleotide N given amino acide A at position p.
Kaplan. et al., PLoS Comput Biol, 2005

47. EM algorithm Iterative EM algorithm E-step M-step Local optima
Exact binding locations for all protein-DNA pairs
recognition preferences: Pp(N|A)
E-step
Compute expected posterior probability of binding locations, based on current preferences
M-step
Update DNA-recognition preferences to maximize the likelihood of current binding locations based on the distribution of possible binding locations in previous E-step
Local optima

48. Estimate DNA-recognition preferences
Figure 2. Estimating DNA-Recognition Preferences
The DNA-recognition preferences are estimated from unaligned pairs of transcription factors and their DNA targets [2] (above). The EM algorithm [13] is used to simultaneously assess the exact binding positions of each protein–DNA pair (bottom right), and to estimate four sets of positionspecific DNA-recognition preferences (bottom left).
Kaplan. et al., PLoS Comput Biol, 2005

49. Apply on TFs from the same family
Figure 3. Predicting the DNA Binding Site Motifs of Novel Transcription Factors
The protein’s DNA-binding domains are identified using the Cys2His2 conserved pattern (top left). The residues at the key positions (6, 3, 2 and 1) of each finger (marked in red in the bottom left panel) are then assigned onto the canonical binding model (bottom right), and the sets of position-specific DNA-recognition preferences (top right panel) are used to construct a probabilistic model of the DNA binding site. For example, the lysine at the sixth position of the third finger faces the first position of the binding site (dotted blue arrow). We predict the nucleotide probabilities at this position using the appropriate recognition preferences (dotted black arrow).
Kaplan. et al., PLoS Comput Biol, 2005

50. Evaluation compatible with experimental results
10-fold cross validation
genome-wide scan of Drosophia melanogaster
29 canonical Cys2His2 TFs
GO Enrichment of predicted target genes
21 enriched with at least one GO term.
mRNA expression profile of target genes
21 showed significant associations in at least one embryogenesis experiment.

51. Compare with other preferences
Kaplan. et al., PLoS Comput Biol, 2005

52. Summary Family-wise approach
Combine structure information with sequence data
Learn context-specific AA-NT recognition preferences
Predict binding preferences of new protein
Identify TFBSs and target genes

53. Discussion Tradeoff between complexity and accuracy
Canonical model
Extension to other DNA-Binding domain
Restrictions: enough binding-data, common and strict binding model…
Provide a promising way to predict target genes of novel proteins and to understand their function and activity

54. Thank you!
Any question?