Highthroughput SELEXSAGE method for quantitative modeling of transcriptionfactor binding sites

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High-throughput SELEX-SAGE method for
quantitative modeling of transcription-factor
binding sites

• Emmanuelle Roulet, Stephane Busso, Anamaria A.
  Camargo, Andrew H.G. Simpson, Nicolas Mermod,
  Philipp Bucher
• Nature Biotechnology August 2002 Vol. 20 p831-835

BioNetworks Journal Club -- Feb. 24, 2003 Slides
prepared by Alison Hottes
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General Background

• Transcription factors bind to DNA and enhance or
  inhibit gene transcription.
• Transcription factor binding is DNA-sequence
  dependent.

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General Problems

• Want to figure out which transcription factors
  bind where and under what conditions
• This knowledge specifies a large part of the
  connectivity and topology of a cells
  transcriptional network
• From a proteins amino acid sequence or structure
  we cant currently predict its binding
  specificity.

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Common Approaches

• Take a few known binding sites for a
  transcription factor and generalize to a
  consensus sequence.
• Find genes likely regulated by a transcription
  factor (e.g. microarrays). Search the upstream
  DNA sequence of those genes for motifs which
  might be the binding site.
• Take the consensus and see where else it appears
  in a genome.
• In both methods, the accuracy is variable and
  often unknown.

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How complex a model is needed?

• Example Suppose a transcription factor binds to
  a 25 base pair DNA region.
• Option 1 Assign a score for the binding affinity
  for each possible 25mer
• Need 425 scores (completely unrealistic)
• Option 2 Assume each base position acts
  independently and equally.
• Need 325 parameters. (Still a lot.)
• Gaps are more difficult.

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For any model

• Need a lot of data
• How can we get the data?
• What can we do with the data once we have it?
• This paper illustrates one approach for CTF/NFI
• Binds as a homodimer
• Recognizes sequences like TTGGC(N5)GCCAA

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Initial Model

• Weight matrix
• Models multiple binding modes
• Given a sequence, can produce a score.
• How would we generate sequences consistent with
  the model?

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Generating Synthetic Sequences

• Generate a HMM from the weights.
• Model depends on the average score desired.
• Use each HMM to generate sequences.

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Experiment Design

• General plan Find sequences that bind CTF/NFI
  and use them to refine the original model.
• Design question What affinity (low/ medium/
  high) should the sequences have for CTF/NFI?
• Approach Use sets of synthetic training
  sequences to estimate weights. Compare to
  original weights.
• Conclusion Low-affinity sequences give more
  accurate models.

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SELEX Systematic evolution of ligands by
exponential enrichment
SAGE Serial analysis of gene expression
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Controlling Binding Affinity

• Add radioactive medium-affinity 25mer probe.
• Want 50 of probe to be competed away by library
  at each step.
• Stop when library and probe bind equally well ?
  library has medium affinity.

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SELEX Systematic evolution of ligands by
exponential enrichment
SAGE Serial analysis of gene expression
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Affinity of Each Cycle

• Fraction of binding sequences increases each
  repetition.
• Average affinity of binding sequences is
  maintained.

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New Model
HMM from SELEX 3
Weight matrix model.

• Each half-site is now 6 bases.
• Model is less tolerant of spacing variation.
• Weights shifted (e.g. now more tolerant of
  adenines in positions 2 and 4).

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Cross-validation

• Model is reasonably stable to sampling variations
  in the dataset.

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Experimental Verification

• Scores correlate well with binding affinity.
• Used model to search Eukaryotic Promoter Database
  for binding candidates. Showed CTF/NFI induction
  of some candidates.

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Non-independence Between Bases

• Checked SELEX sequences for dinucleotide
  correlations.
• Found dependencies. Add less than 1 bit to 12
  bit model.

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Comments and Questions

• Did the improvement justify the work?
• Should the background DNA sequence be modeled?
• Could this be done for an arbitrary transcription
  factor?
• Is it necessary to know a medium affinity
  sequence?
• Must know binding partners.
• Must know what modifications (e.g.
  phosphorylation) are needed for binding.
• Need extract with active protein