Review: RECOMB Satellite Workshop on Regulatory Genomics

1
Review RECOMB Satellite Workshop on Regulatory
Genomics

• (Held March 26-27, 2004)

2
Workshop Themes/Trends

• More comprehensive evaluations of motif-detection
  algorithms
• Making more effective use of comparative
  mapping/evolution data
• Models that explain rather than just describe
• Moving from binding motifs to entire regulatory
  modules
• Methods are simple not sophisticated

3
Outline

• Jim Kadonaga, University of California, San
  DiegoThe MTE, a New Core Promoter Element for
  Transcription by RNA Polymerase II 
• Rotem Sorek, Compugen and Tel Aviv UniversityThe
  "promoters" of splicing Intronic sequences that
  regulate alternative splicing
• Yitzhak Pilpel, Weizman InstituteRevealing the
  architecture of genetic backup circuits through
  inspection of transcription regulatory networks
• Ron Shamir, Tel Aviv UniversityRevealing
  selection patterns in the evolution of yeast
  transcription regulation
• Michael Eisen, Lawrence Berkeley National Lab
  Evolutionary Signatures of Regulatory Sequences

4
A New Core Promoter Element for Transcription by
RNA Polymerase II(Jim Kadonaga)

• The majority of transcription activity is
  regulated by sequence-specific DNA-binding
  factors, which are thus the focus of the bulk of
  current research on regulation, however...
• The ultimate target of all of this action is the
  core promoter, which also plays a part in
  regulation

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• Core promoter
• Encompasses TSS
• Directs RNA polymerase II
• Most well-known component is the TATA box

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• Core promoter
• Encompasses TSS
• Directs RNA polymerase II
• Most well-known component is the TATA box

Only about 30-40 of promoters contain a TATA
box! Whats going on the rest of the time?
7
Finding Novel Promoter Elements

• Experimentally investigated binding in those
  promoters with no TATA-box
• found novel promoter element DPE
• Large scale motif detection of 2000 core
  promoters in Drosophila (Ohler et al, 2002)
• Plotted distance of top 10 motifs to TSS
• four motifs had clear peak TATA, Inr, DPE and
  ...
• a novel promoter element MTE

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The Core Promoter gets a new look
MTE Motif Ten Promoter Element
(Kadonaga, powerpoint slides)
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DPE and MTETwo newly Identified Promoter Elements

• Conserved from Drosophila to human (unknown
  whether occur in yeast)
• Very sensitive to spacing to Inr motif
• experimentally found TSS (papers not reliable)
• single insertion/delection between motifs causes
  7-fold reduction in transcription
• Inr and DPE (or MTE) bound cooperatively by TFIID
  
• first step in transcription initiation

10
TATA gets top billing but...

• In Drosophila (out of 205 core promoters)
• TATA and DPE 14
• TATA only 29
• DPE only 26
• Neither 31
• TATA, DPE, and MTE can all
• independently support transcription
• compensate for mutation in one other

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And finally... regulation.

• NC2 previously known to repress TATA-dependent
  transcription unexpectedly found to activate
  DPE-dependent transcripton
• Studied 18 enhancers and estimate that about 25
  exhibit some specificity for DPE or TATA
• Similar work in progress for MTE

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The Promoters of Splicing (Rotem Sorek)

• In general it is not known how alternative
• splicing (AS) is regulated
• A few known splicing regulatory proteins
• like TFs they are sequence-specific, but they
  bind to RNA not DNA
• binding motif (usually 4-10 nt) can be located in
  exon or intron
• can act as enhancers or silencers
• Evidence for combinatorial regulation

13
The typical motif in a haystack

• Most work on finding splicing
  factor motifs focuses on exons
• short enough that mutation studies feasible
• Introns too long, require a computational
  approach
• Compiled training dataset
• 250 AS exons, AS both in mouse/human
• large set of constituitively spliced (CS) exons,
  conserved across human/mouse

ATTCA
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Sorek and Ast, Genome Research 2003
15

• Their Primary Finding there tends to be
  significantly more conservation in introns
  surrounding AS exons than CS exons
• On average about 100 bases on either side of each
  exon are conserved, compared to around 7 bases
  for constituitively spliced exons
• Whats the explanation?
• multiple binding motifs?
• helping to determine secondary structure in RNA,
  which helps lead to correct splicing?

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Predicting Alternative Splicing

• Additional Predictive features
• Higher conservation around exon
• Higher conservation of exon itself (motifs?)
• Shorter exons
• Exons that are a multiple of 3
• Method somehow chose one threshold for each
  feature?
• Performance scanned human genome, predicted 1000
  AS exons (incl training data?)
• 70 had EST evidence of AS vs 6-7 baseline
• Lab test showed that 7/15 (randomly?) selected
  from remaining 30 are AS in at least one of 15
  tissues
• Significance estimate splicing promoters cover
  3x106 bp

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Genetic Backup Circuits(Kafri and Pilpel)

• Fact single gene knockouts often have little or
  no phenotypic effect
• 10 lethal in worm
• 27 lethal in yeast
• Question Can we better understand the mechanisms
  of genetic backup?
• Task Predict whether a knockout will be lethal
  or not

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Duplicates Suggest Redundancy

• Genes with duplicates are less likely to be
  essential
• But clearly this doesnt tell the whole story
• lethal genes can have duplicates
• nonessential genes often have no duplicate

(Gu, Z. et al Nature 2003)
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Function of Duplicate Matters

• Compute dispensability of yeast genes
• growth rate after knockout compared to mean
  growth rate, averaged over many conditions
• Compared GO functional annotations of highly
  similar genes. Found higher dispensability when
• higher functional similarity (Resnik info
  content)
• little functional similarity but high sequence
  similarity (Blast E-values)

20
Similarity of Expression

• 40 time series, 500 timepoints
• In each condition calculated correlation of
  expression profiles of each pair of paralogous
  genes
• Average correlation suggests
• backup is best provided by genes which do not
  share expression patterns

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How can we explain this unexpected result?

• Classify pairs into
• negative correlation
• never similarly expressed
• positive correlation
• always similarly expressed
• no correlation
• never similarly expressed or
• similarly expressed in certain conditions

22
Variability of Expression

• Use stdDev to quantify consistency of correlation
  across conditions

23
Goldilocks and the three little paralogs
Expression correlated in only a subset of
conditions Just Right
Always Same Expression Too Similar
Never Same Expression Too Diverged

• Optimal backup requires the ability to switch
  between similar and dissimilar expression in a
  condition dependent manner

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Predictions about the Past...

• Hypothesized Duplication Mechanism
• duplication occurs
• leads to nonstable redundancy
• quickly followed by either
• mutation and loss of one of the duplicates
• subfunctionalization leading to stable redundancy
• Hypothesize two distinct types of
  subfunctionalization
• mutation of coding region leading to functional
  divergence
• mutation of control region leading to divergence
  of expression

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Need for Regulatory Flexibility

• This second type of subfunctionalization would
  entail a quite significant regulatory challenge
  if the paralogs are to provide backup for one
  another
• Upon mutation of B, A must be turned on in the
  conditions that would normally require B
• Postulate that
• this regulatory challenge is met when a gene has
  a significant amount of regulatory diversity
  (i.e. different TF motifs)
• backup asymmetry arises when one of the genes has
  few motifs (Kellis suggests otherwise?)

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Experiments, but no hard numbers

• Claim the capacity of genes to respond at the
  transcriptional level when their counterpart is
  deleted is central to their ability to provide
  backup
• Most paralogs downregulated when other gene is
  knocked out (cross-hybridization?)
• lower stdev -gt down regulation
• Claim that asymmetry of backup capability can be
  predicted based on number of transcription factor
  binding sites.
• Gene that has the larger number of motifs is the
  one that is capable of providing a backup to the
  other
• Genes with few motifs are parasites cant
  backup
• Claim an improved ability to predict effect of
  double knockouts

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A Question

• They claim that only when the genes diverge in
  function will they be maintained in evolution.
• But if the duplicated pair can compensate for
  each others function then wont there be little
  selection pressure to maintain both copies?

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From General Conservation to Specific Motifs

• Searched conserved intronic regions for
  overrepresented hexamer
• literature search for most significant hexamer
  shows that hexamer mentioned as an AS motif in
  six papers
• Next steps
• identify the consensus sequences of additional
  motifs
• learn tissue/developmental specificity for each
  motif

29
Revealing Selection Patterns in the Evolution of
Yeast Transcription Regulation(Amos Tanay, Irit
Gat-Viks and Ron Shamir)

• Identifying TF binding sites is hard
• Even harder to predict more complex interactions
• rarely a binary switch
• not a linear relation between affinity and
  acivation
• different binding affinities can lead to
  different results (e.g. P53 can lead to apoptosis
  or rescue)
• Conservation indicates functionality
• Evolution dynamics disclose details of
  functionality

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An AnalogyImagine we didnt know the genetic
code, but just the length of the codes

• We know that synonymous substitutions are more
  common in coding regions than nonsynonymous
  substitutions

• build a network where each 3-letter nt string is
  represented by one node
• put an edge between nodes where the thickness of
  the edge represents the frequency of mutations in
  aligned coding regions of related organisms
• see strongly connected components comprised of
  nodes which all code for the same amino acid

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A Simple Approach

• Chose to use the four recent genomes of simple
  yeasts (promoter regions are relatively short)
• Identified 4000 promoters and aligned them using
  ClustalW
• Use simple window scanning method to identify all
  motifs of size 8
• Simple parsimony method to infer ancestral
  sequences at each node in the phylogeny

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A Simple Approach (2)

• Calculate background substitution rate
• 16 parameter background model for each branch in
  phylogeny
• For each motif, compute 8 tables of site-specific
  substitution rates
• simply count observed substitutions at each site,
  summed over all branches of the tree and all
  instances of the motif
• normalized substitution rate log of ratio of
  observed substitutions over expected substitutions

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Building a Selection Network

• Each node represents an 8mer motif
• Connect all motifs that are 1 substitution apart
• if substitution rate is positive, dark edge
• if substitution rate is negative, light edge
• if not enough data, very thin edge

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images taken from http//www.cs.tau.ac.il/amos/p
romoter_evo/
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• Did some larger scale evaluations based on ChiP
  and gene expression data
• Also some anectodal results

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Matrix of Substitutions from the Motif Concensus
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Evolutionary Signatures of Regulatory Sequences
(Michael Eisen)

• Examples of Evolutionary Signatures
• coding sequence conserved conserved variable
• structural RNA, nt that basepair are coevolving
• What are the evolutionary constraints
• imposed on sequences by TF binding?
• Aligned 4 yeast species
• for each base in genome, estimate evolutionary
  rate (very noisy estimates)

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Analyze the pattern of rate variation across the
entire binding site
Moses et al Evol Biol 2003
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Position-specific Rate Variation

• The pattern of rate variation across the entire
  binding site for a particular TF
• within one genome
• across genomes

40
Position-specific Rate Variation

• The pattern of rate variation across the entire
  binding site for a particular TF
• within one genome
• across genomes
• Clearly due to structural constraints
• protein contacts
• even when we know theres no contact, theres DNA
  bending issues....

Highly Correlated
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These signatures are missing from current
motif-prediction programs

• Although this isnt a particularly suprising
  result, many predicted motifs (e.g. from MEME
  etc.) do not display this TFBS signature
• could use as a filter, or incorporate it more
  directly (theyre working on this currently?)
• Different families of TF have different
  signatures
• Eisen thinks the community is still
  underutilizing this information

42
Make better use of comparative data by using an
explicit evolutionary model

• Is there likely to have been a TFBS in the
  ancestor?
• build a PSSM representing the chemical
  contribution of each base to the binding
  specificity
• use Halpern and Bruno model to predict how the
  TFBS will evolve given proposal selection model

43
Make better use of comparative data by using an
explicit evolutionary model
Moses et al Evol Biol 2003
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Larger Cis-Regulatory Sequences

• Known binding patterns in Drosophila have low
  information content
• find a sequence match for each TFBS before almost
  every gene in the genome
• Build a statistical model to identify significant
  clusters of binding sites in windows of arbitrary
  size
• improved detection of cis-regulatory modules
• experimental results still show many false
  positives
• Use comparative data to discriminate real
  clusters from false ones

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How to use comparative data

• Conservation in Drosophila pseudoobscura isnt a
  good indicator of functionality
• all real and fake clusters have very high overall
  sequence conservation, including their flanking
  regions (a surprise)
• However...
• the actual binding sites are often not conserved
• even one or two mutations can destroy a binding
  site
• conservation of binding site density
• is a useful indicator of function

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An Impassioned Speech on the Evolution of the
Scientific Journal

• If you publish your work in a journal like
  Science which fewer and fewer people in the world
  have access to you run a really big risk of being
  the next Mendel and that your work will languish
  in obscurity
• Dont publish in a journal that takes your
  writing, your ideas, thoughts and paper and
  claims ownership of them and then only doles them
  out to a relatively narrow bunch of people who
  have enough money to pay for them..solely to
  promote the financial health of the journal...
• Dont be like Microsoft... publish in Public
  Library of Science or another freely available
  journal

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For More Information

• Most of the talks I picked were invited talks
• For the workshop there there is often only an
  abstract
• Video feed is available online
  http//www.calit2.net/multimedia/recomb2004videos.
  html
• Many have papers that have just come out or are
  about to come out with additional details...
  check the authors webpages

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Variability of Expression

• Best backup provided by duplicates which have
  similar expression patterns in only a subset of
  conditions

49
Evolution and Larger Cis-Regulatory Sequences

• what are enhancer? whole regions of binding
  sites?
• how are Drosophila enhancers organized
• only 5 binding sites whose specificities are well
  characterized from experim. studies
• low information content
• find them all over the genome
• Clusters of binding sites -gt Surrogate for
  regulatory function
• Shown previously that if look for clusters of
  these sites
• all identified regions overlap known enhancers
• dont find anything else
• then I dont understand next study with 39
  clusters

50

• Found 39 clusters
• 9 overlap known enhancers
• 28 tested experimentally
• 6 clearly regulating nearby gene
• 3 shown some regulatory role perhaps
• remainder dont appear to be real (but could have
  wrong promoter? look back at donoga talk)
• Whats difference between real and fake?
• use comparative mapping

51

• Used two flies (which ones)
• distant enough based on coding region
  conservation that expect to see conservation only
  of funtionally conserved regions
• not the case
• all real and fake clusters have very high overall
  sequence conservation, including their flanking
  regions (why?)

52

• However,
• binding sites not conserved
• one or two mutaitons enough to destroy a binding
  site
• measure conservation of binding site density
• show graph (3718)
• summary (3921)
• In more distantly related species
• alignment more of an issue
• binding sites will move around more
• been shown that huge binding site turnover will
  have 2 separate ways to make the same enhancer
• no sequence identity but in experimental studies
  can replace each other?