Transcript Literature two-hybrid systems

Gene expression and the
transcriptome II
SAGE
• SAGE = Serial Analysis of Gene Expression
• Based on serial sequencing of 15-bp tags that
are unique to each and every gene
• SAGE is a method to determine absolute
abundance of every transcript expressed in a
population of cells
SAGE
• 15-bp gene-specific tags are produced by elegant
series of molecular biology manipulations and then
concatenated into a single molecule (string) for
automated sequencing
• By sequencing the concatenated fragments, the
number of copies of each tag can be counted
• A list of each unique tag and its abundance in the
population is assembled
SAGE
• At least 50,000 tags are required per sample
to approach saturation, the point where each
expressed gene (eukaryotic cell) is
represented at least twice
• SAGE costs about $5000 per sample
• Too expensive to do replicated comparisons
like is done with microarrays
Transcript abundance in typical
eukaryotic cell
• <100 transcripts account for 20% of of total mRNA
population, each being present in between 100 and
1000 copies per cell
• These encode ribosomal proteins and other core
elements of transcription and translation machinery,
histones and further taxon-specific genes
General, basic and most important cellular mechanisms
Transcript abundance in typical
eukaryotic cell (2)
• Several hundred intermediate-frequency
transcripts, each making 10 to 100 copies,
make up for a further 30% of mRNA
• These code for housekeeping enzymes,
cytoskeletal components and some unusually
abundant cell-type specific proteins
Pretty basic housekeeping things
Transcript abundance in typical
eukaryotic cell (3)
• Further 50% of mRNA is made up of tens of
thousands low-abundance transcripts (<10),
some of which may be expressed at less than
one copy per cell (on average)
• Most of these genes are tissue-specific or
induced only under particular conditions
Specific or special purpose products
Transcript abundance in typical
eukaryotic cell (4)
Get some feel for the numbers (can be a factor 2 off
but order of magnitude about right)
If
• ~80 transcripts * ~400 copies = 32,000 (20%)
• ~600 transcripts * ~75 copies = 45,000 (30%)
• 25,000 transcripts * ~3 copies = 75,000 (50%)
• Then Total
=150,000 mRNA
molecules
Transcript abundance in typical
eukaryotic cell (5)
• This means that most of the transcripts in a cell
population contribute less than 0.01% of the
total mRNA
• Say 1/3 of higher eukaryote genome is
expressed in given tissue, then about 10,000
different tags should be detectable
• Taking into account that half the transcriptome
is relatively abundant, at least 50,000 different
tags should be sequenced to approach saturation
(at least 10 copies per transcript on average)
SAGE analysis of yeast (Velculesco et al.,
1997)
Fraction of all transcripts
1.0
17%
38%
45%
0.75
0.5
0.25
0
1000
100
10
1
Number of transcripts per cell
0.1
SAGE quantitative comparison
• A tag present in 4 copies in one sample of
50,000 tags, and in 2 copies in another sample,
may be twofold expressed but is not going to be
significant
• Even 20 to 10 tags might not be statistically
significant given the large numbers of
comparisons
• Often, 10-fold over- or under-expression is
taken as threshold
SAGE quantitative comparison
• A great advantage of SAGE is that method is
unbiased by experimental conditions
• Direct comparison of data sets is possible
• Data produced by different groups can be
pooled
• Web-based tools for performing comparisons of
samples all over the world exist (e.g. SAGEnet
and xProfiler)
Genome-Wide Cluster Analysis
Eisen dataset
• Eisen et al., PNAS 1998
• S. cerevisiae (baker’s yeast)
– all genes (~ 6200) on a single array
– measured during several processes
• human fibroblasts
– 8600 human transcripts on array
– measured at 12 time points during serum
stimulation
The Eisen Data
• 79 measurements for yeast data
• collected at various time points during
– diauxic shift (shutting down genes for
metabolizing sugars, activating those for
metabolizing ethanol)
– mitotic cell division cycle
– sporulation
– temperature shock
– reducing shock
The Data
• each measurement represents
Log(Redi/Greeni)
where red is the test expression level, and green is
the reference level for gene G in the i th experiment
• the expression profile of a gene is the vector of
measurements across all experiments [G1 .. Gn]
The Data
• m genes measured in n experiments:
g1,1 ……… g1,n
g2,1 ………. g2,n
gm,1 ………. gm,n
Vector for 1 gene
Eisen et al. Results
• redundant representations of genes cluster
together
– but individual genes can be distinguished from
related genes by subtle differences in
expression
• genes of similar function cluster together
– e.g. 126 genes strongly down-regulated in
response to stress
Eisen et al. Results
•
126 genes down-regulated in response to
stress
–
–
112 of the genes encode ribosomal and other
proteins related to translation
agrees with previously known result that yeast
responds to favorable growth conditions by
increasing the production of ribosomes
Partitional Clustering
• divide instances into disjoint clusters
– flat vs. tree structure
• key issues
– how many clusters should there be?
– how should clusters be represented?
Partitional Clustering from a
Hierarchical Clustering
we can always generate a partitional clustering from a
hierarchical clustering by “cutting” the tree at some level
K-Means Clustering
• assume our instances are represented by vectors of
real values
• put k cluster centers in same space as instances
• now iteratively move cluster centers
K-Means Clustering
• each iteration involves two steps:
– assignment of instances to clusters
– re-computation of the means
K-Means Clustering
• in k-means clustering, instances are assigned to
one and only one cluster
• can do “soft” k-means clustering via Expectation
Maximization (EM) algorithm
– each cluster represented by a normal distribution
– E step: determine how likely is it that each cluster
“generated” each instance
– M step: move cluster centers to maximize likelihood of
instances
Protein-protein interactions
Protein-protein interaction
If you want to know whether any particular proteins bound
to protein X.
Then such proteins can be found by the yeast two-hybrid
system.
The two-hybrid system allows in vivo detection of proteinprotein interactions as well as the analysis of the affinity of
these interactions.
Protein-protein interaction
Two-hybrid technology exploits the fact that transcriptional
activators are modular in nature.
Two physically distinct functional domains are necessary to
get transcription:
(1) a DNA binding domain (DBD) that binds to the DNA of
the promoter and
(2) an activation domain (AD) that binds to the basal
transcription apparatus and activates transcription.
Protein-protein interaction
In the yeast two-hybrid system, the known gene encoding
X, is cloned into the "bait" vector.
In this way, the gene for X is placed into a plasmid next to
the gene encoding a DNA-binding domain from some
transcription factor.
For instance, if the gene for X is cloned into the
pHybLex/Zeo vector, X would be expressed as a fusion
protein containing bacterially-derived LexA DBD.
Protein-protein interaction
Separately, a second gene (or a library of cDNAs encoding
potential interactors), Y, is cloned in frame adjacent to an
activation domain of a different transcription factor. For
instance, it could be inserted next to the DNA encoding the
B42 activation domain (AD) in a "prey" vector such as
pYESTrp2.
Protein-protein interaction
Thus, in one strain of yeast, a known protein X is fused to
the DNA binding domain of a transcription factor; and in
another strain, unknown proteins are fused to the activation
domain of another transcription factor. If one of the
unknown proteins combines with X, it will bring the AD
over to the DBD, and transcription will be activated. So the
plasmids containing the "bait" (known protein/DBD) and
"prey" (unknown protein/AD) are then placed into a yeast
strain, where a marker gene has a promoter containing the
sequence bound by the bait protein DBD.
Protein-protein interaction
However, in order to form a working transcription factor, it
needs the AD provided by the "prey." The only way that it
can get this activation domain is if the known protein X
combines with some unknown protein Y that is carrying the
AD. If the X and Y proteins interact, the B42 AD is brought
into proximity of the LexA DBD and transcription of the
reporter gene is activated. The activation of the reporter
gene can be screened by enzyme activity, light release, or
cell growth, depending on the type of reporter gene
activated.
Schematic of the two-hybrid system.
(A). Vector is made containing transcription factor DBD plus
protein X. When placed into a yeast with a reporter gene, this
fusion protein can bind to the reporter gene promoter, but it
cannot activate transcription.
(B) A second vector is made where unknown cDNAs are
placed adjacent to the activation domain of a transcription
factor. When placed into a yeast strain containing the reporter
gene, it cannot activate transcription, since it has no DNA
binding domain.
(C) When the two vectors are placed into the same yeast, a
transcription factor is formed that can activate the reporter
gene–if the protein made by the second plasmid binds to the X
protein.
Literature two-hybrid systems
1. Fields, S and Song, O. 1989. A novel genetic
system to detect protein-protein interactions.
Nature 340:245 -246.
2. Gyuris, J., Golemis, E., Chertkov, H., and Brent,
R. 1993. Cdi1, a human G1 and S phase protein
phosphatase that associates with Cdk2. Cell. 75:
791-803.