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Outline

• Background
• Previous experimental work
• Goals
• Results
• Future directions

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Regulation of Transcription

• One of the best understood mechanisms of
  transcription regulation is the action of
  regulatory proteins, binding to the up-stream
  region of a gene act as either promoters or
  suppressors of transcription

Transcription Factor
Transcription Factor
Gene
Gene
ACAGTGA
Protein
Protein

• Regulation of transcription is time, cell and
  tissue specific
• 510 of the total coding capacity of metazoans
  is dedicated to proteins that regulate
  transcription

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Transcription Factors (TF) - Factsheet

• Found in all living organisms
• Have affinity for short, degenerate DNA
  sequences (5-15 bp)
• Contain one or more DNA binding domains (DBDs)
• Mutated TF genes have been shown to cause
  numerous diseases (Eg Haemophilia B Leyden)
• Potential Targets for several therapeutic drugs
  (Eg breast cancer)

http//www.biochem.arizona.edu/
Butt et al 1995, Latchman et al 2000
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Promoter architecture

• Organization of promoter motifs represents a
  "footprint" of the
• transcriptional regulatory mechanisms
• Complex transcriptional control modules

Levine et al, 2003
Active promoters have a unique 3-dimensional
structure
Changing the order or spacing of transcription
factor binding sites (TFBS) can change the
overall structure of the promoter and thus affect
transcription
Werner et al, 2003
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Transcription regulation Example 1
Nature Genetics  29, 153 - 159 (2001)
Microarray data annotation of the yeast genome
novel TFBS combinations in the promoters of
co-regulated genes Elegantly demonstrate that a
relatively small number of TFs can be responsible
for a complex set of expression patterns in
diverse conditions
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Transcription regulation Example 2
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Transcription regulation Example 3
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Why we are doing what we are doing ?

• Promoter features provide clues to gene function
  that are not
• obvious from the protein sequence alone
• Genes having similar expression patterns may
  contain common motifs in their promoter regions.
  Thus, common set of TFs are likely to control
  these co-expressed genes
• Understanding the gene regulation in livestock
  species is still in primitive stages, so this
  study serves as a starting point for livestock
  researchers
• Druggable targets allowing to us make animal
  more resistant to
• one or more parasites

CSIRO. Promoter sequence analysis of
differentially expressed genes
Vilo et al., 2000, Klok et al. 2002
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A model gene expression regulatory network
distinct transcription factors
potential target genes

• Colour of the rectangular oval indicates
• which transcription factors is regulating its
• expression in response to the nematode
• parasite challenge
• Arrows point from each transcription
• factors to its regulated genes

Curr Opin Genet. 2002 Apr,130
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Goals generic

• Elucidation of transcriptional network
  containing regulatory
• apparatus (TF and their target genes) by applying
  computational and
• experimental approaches
• Focus on TFs that trigger immune response in
  mammalian host
• when challenged from a parasite

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Goals - specific

• To benchmark currently available methods and
  establish a broad protocol
• for regulatory sequence analysis
• To conduct a comprehensive analysis of promoter
  sequences for DE genes
• in sheep (pilot study)
• To extensively use human promoter sequence data
  (well studied)
• using comparative genomics approaches
• To apply this protocol to other related projects
  at CSIRO as a starting point
• to understand transcription regulation
  capability development - expand to
• P-Health and Food Futures Flagships
• Plenty of gene expression data available in the
  division on livestock species
• from previous studies, take it further and give
  it a regulatory spin to gain
• additional insights of the dataset

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Pilot study summary
Evaluation of currently available computational
resources for identification and analysis of
regulatory sequences Established a
comprehensive approach for the analysis of
regulatory sequences Applied this approach for
the pilot study and identified putative
TFBS Experimental validation of putative TFBS
In-vitro In-vivo Selection of master
regulators transcription factors ChIP-Seq
experiments to identify genome wide TFBS for
master regulators Parallel large scale
computational genome wide TFBS prediction (novel
promoter models, conserved TFBS etc)
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Previous work
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Data

• Sets of differentially expressed (DE) genes in
  resistant and susceptible animals following
  nematode challenge

XDH cluster
TLR cluster
Cathepsin Cluster
DUOX Cluster
Transcriptional co-regulation
Common regulatory elements
Co-expression
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Promoter sequence analysis a combinatorial
approach
Desired genes
Retrieve 2KB upstream region from TSS
Comparative Promoter Analysis Compare
oligonucleotides, TFBS with closely related
mammals (eg human)

• Search for TFBS
• 1. Known TFBS data (database)
• 2. Identify common framework
• 3. Position weight matrices (PWM)
• 4. Literature evidence

Identify statistically over-represented
oligonucleotides in each geneset
Rank the potential regulatory elements
Experimental Validation
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Over-represented consensus binding sites
discovered in 22 Toll Like Receptor (TLR)
pathway genes
Motif 1 TCAGAAA
P-value 3.5e-05
Motif 2 AGAGAAA
P-value 3.5e-06
Motif 3 GGGAGGA
P-value 2.1e-05
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Promoter sequence analysis - schema
Desired genes
Retrieve 2KB upstream region from TSS
Comparative Promoter Analysis Compare
oligonucleotides, TFBS with closely related
mammals (eg human)

• Search for TFBS
• 1. Known TFBS data (database)
• 2. Identify common framework
• 3. Position weight matrices (PWM)
• 4. Literature evidence

Identify statistically over-represented
oligonucleotides in each geneset
Rank the potential regulatory elements
Experimental Validation
CSIRO. Promoter sequence analysis of
differentially expressed genes
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DLL4 gene as an example
Promoter region conserved in mammalian species
8 TFBS 10 mammals
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Eukaryotic transcriptional apparatus
Levine et al, 2003
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Multiple alignment of transcription factor
binding sites (TFBS) conserved across species
GC box
TATA box
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Experimental validation
Computationally predicted TFBS
Protein extraction
Design primers for promoter region
Enrichment for nuclear proteins (focus on TFs)
PCR
Quantitative amplification of PCR product
Mix PCR product and enriched protein sample
Check for in-vitro binding
Mass-spectrometry to identify TFs
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Enrichment of cytoplasmic and nuclear proteins
1
2
3
1 Mol.Wt. Markers 2 7123 Nuclear 3 7123
Cytoplasmic
Animal IDs

• Nuclear extract is enriched with activated
  transcription factors

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Mobility Shift Assays MyD88 Promoter
MyD88 Acquired-R
MyD88 Naive
Bound
Bound
Unbound
Unbound
The 5 targets pointing right are probably the
best TF candidates
Disease vs Healthy
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Future directions

• Computational
• To establish a roadmap for regulatory sequence
  analysis in livestock species

Experimental validation for initial
Results ChIP-Sequencing following the
identification of master regulators (TFs) that
confers resistance Downstream characterization
of regulatory network for immune response
bigger picture
Livestock species
CSIRO. Promoter sequence analysis of
differentially expressed genes
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Conclusions

• We believe that the TFBS identified in this
  study have regulatory potential
• Binding of TFs to these motifs might explain the
  differential expression observed following
  nematode challenge
• Functional variation in these motifs is
  therefore likely to contribute to an individuals
  ability to resist infection

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Acknowledgments
Aaron Ingham CSIRO Livestock Industries
Antonio Reverter CSIRO Livestock Industries
Michael Lees Moira Menzes

• OCE Post-doctoral fellowship
• SheepGenomics