2. Regulation of Gene Expression
Pre-transcriptional regulation
chromatin compaction eg deacetylation, methylation
transcriptional initiation ie transcription factors to activate or repress
alternative promoters =?> alternative transcripts
During transcription
number of transcripts made, rate of transcription
alternative mRNA splicing =?> splice variants (alternative transcripts)
regulation of mRNA stability (3’UTR, miRNA etc)
Post-transcriptional regulation
5’UTR regulatory functions not yet fully understood
regulation of translation initiation
during folding of the protein
later control of protein activity (acetylation, phosphorylation etc)

3. What is a promoter
A DNA sequence that is involved in the regulation of a gene.
It has a binding site for RNA polymerase and binding sites for transcription factors.
Was thought to be immediately upstream of a gene, but in fact is symmetrical around the transcriptional start site (ENCODE, 2007)
Activity of protein complexes bound to promoter regions can
activate a gene (switch on)
or repress its transcription (switch off)
or somewhere in between (dimmer switch)

4. Translation initiation site Initiation codon ATG
5’UTR
Translation initiation site
Initiation codon
ATG
promoter
Exon 1
Exon 2 5’
TSS
Transcriptional Start Site 3’
Exon 1
Exon 2
Transcription factor binding sites
TFBSs

5. Classifying Promoters
By distance from TSS
but where is the TSS
By signal in ATCG content (Landolin et al., 2013)
but does this apply in all species and cell types?
By concentration of TFBSs along the length of the gene, around the TSS or several TSSs
but what if these signals are only relevant in certain tissues at certain times?

6. By distance from TSS
Length of a promoter varies greatly. Usually has many transcription factor binding sites along it – but spacing can be large.
BASIC CATEGORIES OF PROMOTERS
Core promoter is the region ± 40 from the TSS;
Proximal promoter is the region ± 250 from the TSS. Many current promoter analysis studies actually take a promoter region which is
± 500, ± 1000 or even ± 5000 bases from the TSS.
An enhancer is a sequence located several Kb upstream or downstream of a gene that its regulates transcription.

7. Transcription Factors
Activators or Repressors
and cofactors, chaperones, modifiers
Usually work in large protein complexes
Need 2-4 per promoter
Two TFs may compete for same binding site: e.g. one is repressing, needs to be modified in some way to allow an activator to bind and switch that gene on.
Regulate transcription per tissue, time, physiological state, etc

8. Finding TFBSs
Sequence based. Some literature reports include protein structure parameters.
Motif finding algorithms abound.
Start with a multiple sequence alignment, most are probabilistic.
PSSM
HMM
Weight array matrix with Markov dependence assumptions
Trees or Baysian networks
Mostly based on assumption that TFBSs are of fixed length
Non-probabilistic models allow variable length through degeneracy
Exon 1
Exon 2
CTGTCCAGAACT
ATGCGGGTACT
GTATCTTAGT

9. Defining TFBSs Regular expression [A/C] C G T N [A/C] {C} T
a G g t a c T t
C c A t a A g t
Alignment a c g t T A g t
a c g t C c A t
C c g t a c g G
_________________ A
Profile C G T
Consensus A C G T A C G T
Regular expression [A/C] C G T N [A/C] {C} T

10. Representing TFBSs If very conserved, easy to define a motif
Consensus or regular expression
Graphical representation (logo)
Frequency counts

11. Confirming TFBSs Found a motif, now search it against TFBS databases
CHIP-seq experimental evidence
Chromatin accessibility
Found a TFBS… stimulus, time, tissue?
SP1, PAX9, HNF1 alpha

12. It’s Complicated Sequence analysis might find several on a promoter
When, where, how…
Include activators and repressors
For shorter TFBSs, lots of false positives
Modules of 3 or 4 work together to regulate the transcription of a gene.
Exon 1
Exon 2

13. Prediction of promoter regions
Closely linked to prediction of ORFs
where there is an ORF there is a promoter (? TATA box)
Two main methods:
- Pattern Driven a concentration of TFBSs
- Conservation Across Species conserved TFBS patterns
Problems with both:
TFBSs are only 5-15bp long, and can be variable
vary between species, and relevance to tissues
methods say nothing about context of the sites, interactions between TFs, or probability that a site is functional
Context= working together or competitive, in series or in parallel, etc

14. Eukaryotic Promoter Database
A collection of experimentally verified TSSs and the promoter regions associated with them.
>When it began
Experimental evidence, one gene at a time. Results using the techniques of the time found that each gene had one TSS and one promoter, upstream of TSS.
>Now
More sophisticated techniques and high-throughput methods, one genome at a time (e.g. 5’ESTs). A gene can have multiple TSSs, multiple promoters, symmetrical around TSS
>How
Partly experimental, partly computational. Recognises promoters by presence of “promoter elements” (TATA boxes, CpG islands, etc)

15. EPD: Three classes of promoters (with experimental evidence)
Single initiation sites (genes with one TSS)
2. Clustered multiple initiation sites (genes with several TSSs close together)
3. Transcriptional initiation regions (several TSSs far apart)
These genes may have alternative promoters

16. Which one is it?
Experimental methods for finding TSSs rely on specialized sequencing of 5’ end of full length clones
Multiple TSSs are always found per gene, which one is the “real” one? Depends on tissue and time, physiological state, stimulus, etc
For your research, do you:
Take the TSS farthest 5’end from the ATG (translation initiation codon) or
the TSS most frequently found before the ATG?
Or see if both apply, and assign multiple TSSs and promoters accordingly? EPD and DBTSS both can help you do that

17. Web Tools for Promoter Analysis
Lots of promoter analysis web tools out there- check date last modified and/or updated, read the paper, test it out, try out more than one.
Many need a multiple alignment of promoter regions as input.
Remember possibility of alternative promoters.
Following slides are a couple of good databases and several tools.

18. Eukaryotic Promoter Database

23. Melina II uses four different pattern searching algorithms for promoter analysis

24. Promoter Analysis Project Example
Best strategy is to conduct a pattern finding search (use more than one web tool for this), followed by conservation analysis across comparable species to identify possible active TFBSs.
Chr Prot(aa) nuclear cytoplasmic
HDAC11 3p   
HDAC1 1p  
HDAC2 6q  
HDAC3 5q   
HDAC8 Xq  
HDAC4 2q  
HDAC5 17q  
HDAC7 12q  
HDAC9 7p  
HDAC6 Xp   
HDAC10 22q   

25. Predicted motifs on 2000bp region of HDACs
Predicted motifs on 2000bp region of HDACs. The region 500bp upstream and 100bp downstream of TSS, contains more than half of predicted motif species.

26. The conserved motifs among mammals were identified by footprint
The conserved motifs among mammals were identified by footprint. The pattern of conserved motifs is distinct in different species groups.
(Z. Jiang and S. Khuri using Genomatix software suite)

27. The predicted motifs on HDAC1 were grouped by tissue specificity feature. The motifs we found point to transcription factors that have some tissue and time preferences, which implies distinct expression patterns among the HDACs.