Transcript Computational Biology

Transcription – Gene regulation
The machine that transcribes a
gene is composed of perhaps 50
proteins, including RNA
polymerase, the enzyme that
converts DNA code into RNA code.
A crew of transcription factors
grabs hold of the DNA just above
the gene at a site called the core
promoter, while associated
activators bind to enhancer regions
farther upstream of the gene to rev
up transcription.
Working as a tightly knit machine, these
proteins transcribe a single gene into
messenger RNA. The messenger RNA
winds its way out of the nucleus to the
factories that produce proteins, where it
serves as a blueprint for production of a
specific protein.
http://www.berkeley.edu/news/features/1999/12/09_nogales.html a
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Transcription in E.coli and in Eucaryotes
Procaryotes
Eucaryotes
Genes are grouped into operons
Genes are not grouped in operons
mRNA may contain transcript of
several genes (poly-cistronic)
each mRNA contains only
transcript of a single gene
(mono-cistronic)
Transcription and translation are coupled.
Transcript is translated already during
transcription.
Transcription and translation are
NOT coupled.
Transcription takes place
in nucleus, translation in cytosol.
Gene regulation takes place by
modification of transcription rate
Gene regulation via transcription
rate AND by RNA-processing,
RNA stability etc.
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Promoter prediction in E.coli
To analyze E.coli promoters, one may align a set of promoter sequences by the
position that marks the known transcription start site (TSS) and search for
conserved regions in the sequences.
 E.coli promoters are found to contain 3 conserved sequence features
- a region approximately 6 bp long with consensus TATAAT at position -10
- a region approximately 6 bp long with consensus TTGACA at position -35
- a distance between these 2 regions of ca. 17 bp that is relatively constant
a
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Gene regulatory promoter network
In E.coli, 240 transcription factors have been verified that regulate 3000 genes.
Binding site matrics are available for more than 55 E.coli TFs
(Robison et al. 1998)
In S. cerevisae, genome-wide binding analysis of 106 transcription factors
indicates that more than one-third of the promoter regions that were bound by
regulators were bound by 2 or more regulators.
 Highly connected network of transcriptional regulators.
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Feasibility of computational motif search?
Computational identification of transcription factor binding sites is difficult
because they consist of short, degenerate sequences that occur frequently by
chance.
The problem is not easy to define (therefore: it is „complex“) because
- the motif is of unknown size
- the motif might not be well conserved between promoters
- the sequences used to search for the motif do not necessarily represent the
complete promoter
- genes with promoters to be analyzed are in many cases grouped together by a
clustering algorithm which has its own limitations.
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Strategy 1
Arrival of microarray gene-expression data.
Group of genes with similar expression profile (e.g. those that are activated at
the same time in the cell cycle)  one may assume that this profile ist, at least
partly, caused by and reflected in a similar structure of the regions involved in
transcription regulation.
Search for common motifs in < 1000 base upstream regions.
Sofar used: detection of single motifs (representing transcription-factor binding
sites) common to the promoter sequences of putatively co-regulated genes.
Better: search for simultaneous occurrence of 2 or more sites at a given distance
interval! Search becomes more sensitive.
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Motif identifaction
A flowchart to illustrate the two
different approaches for motif
identification. We analyzed 800
bp upstream from the translation
start sites of the five genes from
the yeast gene family PHO by
the publicly available systems
MEME (alignment) and RSA
(exhaustive search). MEME was
run on both strands, one
occurrence per sequence mode,
and found the known motif
ranked as second best. RSA
Tools was run with oligo size 6
and noncoding regions as
background, as set by the demo
mode of the system. The wellconserved heptamer of the motifs
used by MEME to build the
weight matrix is printed in bold.
Ohler, Niemann Trends Gen 17, 2 (2001)
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Strategy 2: Exhaustive motiv search in upstream regions
Exploit the finding that relevant motifs are often repeated many times,
possibly with small variations, in the upstream region for the regulatory action to
be effective.

Search upstream region for overrepresented motifs
(1) Group genes based on the overrepresented motifs
(2) Analyze sets of genes that share motifs for coregulation in microarray exp.
(3) Consider overrepresented motifs labelling sets of co-regulated genes as
candidate binding sites.
Cora et al. BMC Bioinformatics 5, 57 (2004)
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Exhaustive motiv search in upstream regions
Exploit
Cora et al. BMC Bioinformatics 5, 57 (2004)
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Exhaustive motiv search in upstream regions
Cora et al. BMC Bioinformatics 5, 57 (2004)
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Exhaustive motiv search in upstream regions
Cora et al. BMC Bioinformatics 5, 57 (2004)
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Recently published tools for promoter finding
Ohler, Niemann Trends Gen 17, 2 (2001)
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Position-specific weight matrix
Popular approach when list of genes available that share TF binding motif;
Good multiple sequence alignment available.
Alignment matrix: lists # of occurrences of
each letter at each position of an alignment
Hertz, Stormo (1999) Bioinformatics 15, 563
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Position-specific weight matrix
Examples of matrices used by YRSA
http://forkhead.cgb.ki.se/YRSA/matrixlist.html
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Exp. Identification of TF binding site: DNase 1 Footprinting
A protein bound to a specific DNA sequence
will interfere with the digestion of that region by
DNase I.
*
*
*
*
An end-labelled DNA probe is incubated with a
protein extract or a purified DNA-binding factor.
The unprotected DNA is then partially digested
with DNase I such that on average every DNA
molecule is cut once.
Denaturing PAGE
Digestion products are then resolved by
electrophoresis.
Comparison of the DNase I digestion pattern in
the presence and absence of protein will allow
the identification of a footprint (protected
region)
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Footprint
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Gel retardation assays
Gel Shifts
Electro Mobility Shift Assay (EMSA)
Band Shift
Incubating a purified protein, or a complex
mixture of proteins e.g. nuclear or cell extract,
with a 32P end-labelled DNA fragment
containing the putative protein binding site
(from promoter region).
Reaction products are then analysed on a nondenaturing polyacrylamide gel.
The specificity of the DNA-binding protein for
the putative binding site is established by
competition experiments using DNA fragments
or oligonucleotides containing a binding site for
the protein of interest, or other unrelated DNA
sequences.
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No protein
*
add protein
*
Non-denaturing PAGE
Retarded
mobility due
to protein
binding
Free DNA probe
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3D structures of transcription factors
1A02.pdb
1AU7.pdb
1AM9.pdb
TFs bind with very
different binding modes.
Some are sensitive
for DNA conformation.
2 TFs bound!
1CIT.pdb
1GD2.pdb
1H88.pdb
http://www.rcsb.org
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DNA conformation
Canonical and mechanically distorted forms of helical DNA
(from left to right: A-DNA, B-DNA, overstretched S-DNA,32
overtwisted P-DNA33).
Conformational fluctuations of a BDNA oligomer with an alternating
GA sequence. The snapshots (100
ps intervals) from a simulation at
300 K using explicit solvent and
counterions show axis and
backbone fluctuations
E. Giudice, R. Lavery (2002) Acc. Chem. Res. 35, 350-357.
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DNA conformation
Induced base opening within B-DNA. Images show the
conformational changes associated with moving thymine
(bold) into the major groove of an oligomer with an
alternating GA sequence.
E. Giudice, R. Lavery (2002) Acc. Chem. Res. 35, 350-357.
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EM low-resolution structure of TF machinery
Single particle images
3D reconstruction of TFIID
Nogales et al. Science (1999)
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Identification of individual components
Position of IIB and IIA on the TFIID structure and
mapping of the TBP. The blue mesh corresponds
to the holo-TFIID, with the A, B, and C lobes
indicated. (A) The green mesh corresponds to the
density difference between the holo-TFIID and the
TFIID-IIB complex. (B) The magenta and green
meshes show the density difference between the
holo-TFIID and the trimeric complex TFIID-IIA-IIB.
The density depicted in light green can be
attributed to TFIIB by comparison with (A), and the
magenta density therefore corresponds to IIA. (C)
The yellow mesh shows the density difference
between the holo-TFIID and TFIID that is bound to
the TBP antibody.
Nogales et al. Science (1999)
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database for eukaryotic transcription factors: TRANSFAC
BIOBase / TU Braunschweig / GBF
Relational database
6 flat files:
FACTOR interaction of TFs
SITE
their DNA binding site
GENE through which they regulate
these target genes
CELL
factor source
MATRIX TF nucleotide weight matrices
CLASS classification scheme of TFs
Wingender et al. (1998) J Mol Biol 284,241
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database for eukaryotic transcription factors: TRANSFAC
BIOBase / TU Braunschweig / GBF
Matys et al. (2003) Nucl Acid Res 31,374
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MatchTM
Search for putative TF binding sites in DNA sequences based on weight
matrices.
Use 2 values to score putative hits:
Matrix similarity score: quality of a match between the sequence and the whole
matrix  [0,1]
Core similarity score: quality of a match between the sequence and the core
sequence of a matrix which consists of the five most conserved consecutive
positions in a matrix  [0,1]
Profile: set of matrices and their cut-offs designed for function-driven searches
Special profiles available for immune-cells, muscle cells, liver cells, and for cellcycle.
Matys et al. (2003) Nucl Acid Res 31,374
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database for eukaryotic transcription factors: TRANSFAC
BIOBase / TU Braunschweig / GBF
Matys et al. (2003) Nucl Acid Res 31,374
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TRANSFAC classification
1 Superclass basic domains
1.1 Leuzine zipper factors (bZIP)
1.2 Helix-loop-helix factors (bHLH)
1.3 bHLH-bZIP
1.4 NF-1
1.5 RF-X
1.6 bHSH
3 Superclass: Helix-turn-helix
4 Superclass: beta-Scaffold
Factors with Minor Groove
Contacts
5 Superclass: others
2 Superclass: Zinc-coordinating DNA-binding domains
2.1 Cys4 zinc finger of nuclear receptor type
2.2 diverse Cys4 zinc fingers
2.3 Cys2His2 zinc finger domains
2.4 Cys6 cysteine-zinc cluster
2.5 Zinc fingers of alternating composition
http://www.gene-regulation.com/pub/databases/transfac/cl.html
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TRANSFAC classification
Eintrag für 1.1 Leuzine-Zippers
http://www.gene-regulation.com
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TRANSFAC classification
http://www.gene-regulation.com
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TRANSFAC classification
http://www.gene-regulation.com
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Summary
Large databases available (e.g. TRANSFAC) with information about promoter sites.
Information verified experimentally.
Microarray data allows searching for common motifs of coregulated genes.
Also possible: common GO annotation etc.
TF binding motifs are frequently overrepresented in 1000 bp upstream region.
Clear function of this is unknown.
(Same as in proline-rich recognition sequences.)
Relatively few TFs regulate large number of genes.
 Complex regulatory network, Thursday lecture.
http://www.gene-regulation.com
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