Download Lecture_7

• Review of important points from the NCBI
lectures.
– Example slides
• Review the two types of microarray platforms.
– Spotted arrays
– Affymetrix
• Specific examples that use microarray technology.
– Gene expression - role of a transcription factor
Web
Access
Text
Entrez
Sequence
BLAST
Structure
VAST
N ucleotide Translated BLAST P rotein
Particularly useful for nucleotide sequences without
protein annotations, such as ESTs or genomic DNA
tblastn
P
N
PPP
PPP
tblastx
PPP
P
N
N
PPP
PPP
PPP
N
Database
PPP
blastx
Query
PPP
Program
Position Specific Score Matrix
(PSSM)
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D
G
V
I
S
S
C
N
G
D
S
G
G
P
L
N
C
Q
A
A
0
-2
-1
-3
-2
4
-4
-2
-2
-5
-2
-3
-3
-2
-4
-1
0
0
-1
R N D C Q E G H I L K M F
-2 0 2 -4 2 4 -4 -3 -5 -4 0 -2 -6
-1 0 -2 -4 -3 -3 6 -4 -5 -5 0 -2 -3
1 -3 -3 -5 -1 -2 6 -1 -4 -5 1 -5 -6
3 -3 -4 -6 0 -1 -4 -1 2 -4 6 -2 -5
-5 0 8 -5 -3 -2 -1 -4 -7 -6 -4 -6 -7
-4 -4 -4 -4 -1 -4 -2 -3 -3 -5 -4 -4 -5
-7 -6 -7 12 -7 -7 -5 -6 -5 -5 -7 -5 0
Serine
scored
0 2 -1 -6
7 0is -2
0 -6differently
-4 2 0 -2
-3 -3 -4 -4
-5 two
7 -4 positions
-7 -7 -5 -4 -4
in-4
these
-5 -2 9 -7 -4 -1 -5 -5 -7 -7 -4 -7 -7
-4 -2 -4 -4 -3 -3 -3 -4 -6 -6 -3 -5 -6
-6 -4 -5 -6 -5 -6 8 -6 -8 -7 -5 -6 -7
-6 -4 -5 -6 -5 -6 8 -6 -7 -7 -5 -6 -7
Active
-6
-6 -5site
-6 nucleophile
-5 -5 -6 -6 -6 -7 -4 -6 -7
-6 -7 -7 -5 -5 -6 -7 0 -1 6 -6 1 0
-6 0 -6 -4 -4 -6 -6 -1 3 0 -5 4 -3
-4 -5 -5 10 -2 -5 -5 1 -1 -1 -5 0 -1
1 4 2 -5 2 0 0 0 -4 -2 1 0 0
-1 1 3 -4 -1 1 4 -3 -4 -3 -1 -2 -2
P
1
-2
-4
-5
-5
-1
-7
-5
-6
-5
-4
-6
-6
9
-6
-6
-4
0
-3
S
0
-2
0
-3
1
4
-4
-1
-3
-4
7
-4
-2
-4
-6
-2
-1
-1
0
T
-1
-1
-2
0
-3
3
-4
-3
-5
-4
-2
-5
-4
-4
-5
-1
0
-1
-2
W
-6
0
-6
-1
-7
-6
-5
-3
-6
-8
-6
-6
-6
-7
-5
-6
-5
-3
-2
Y
-4
-6
-4
-4
-5
-5
0
-4
-6
-7
-5
-7
-7
-7
-4
-1
0
-3
-2
V
-1
-5
-2
0
-6
-3
-4
-3
-6
-7
-5
-7
-7
-6
0
6
0
-4
-3
PSI-BLAST
Create your own PSSM:
Confirming relationships of purine
nucleotide metabolism proteins
query
PSSM
BLOSUM62
Alignment
Affymetrix vs. glass slide based
arrays
•
•
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•
Affymetrix
Short oligonucleotides
Many oligos per gene
Single sample
hybridized to chip
• Glass slide
• Long oligonucleotides
or PCR products
• A single oligo or PCR
product per gene
• Two samples
hybridized to chip
Bacterial DNA microarrays
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Small genome size
Fully sequenced genomes, well annotated
Ease of producing biological replicates
Genetics
Applications of DNA microarrays
• Monitor gene expression
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Study regulatory networks
Drug discovery - mechanism of action
Diagnostics - tumor diagnosis
etc.
• Genomic DNA hybridizations
– Explore microbial diversity
– Whole genome comparisons
– Diagnostics - tumor diagnosis
• ?
Characterization of the stationary
phase sigma factor regulon (sH)
in Bacillus subtilis
• Patrick Eichenberger,
Eduardo Gonzalez-Pastor,
and Richard Losick Harvard University.
• Robert A. Britton and
Alan D. Grossman Massachusetts Institute
of Technology.
What is a sigma factor?
• Directs RNA
polymerase to
promoter sequences
• Bacteria use many
sigma factors to turn
on regulatory
networks at different
times.
– Sporulation
– Stress responses
– Virulence
Wosten, 1998
Alternative sigma factors in B.
subtilis sporulation
Kroos and Yu, 2000
The stationary phase sigma factor: sH
 most active at the transition from exponential growth to
stationary phase
 mutants are blocked at stage 0 of sporulation
• known targets involved in:
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
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
phosphorelay (kinA, spo0F)
sporulation (sigF, spoVG)
cell division (ftsAZ)
cell wall (dacC)
general metabolism (citG)
phosphatase inhibitors (phr peptides)
Experimental approach
• Compare expression profiles of wt and
∆sigH mutant at times when sigH is active.
• Artificially induce the expression of sigH
during exponential growth.
– When Sigma-H is normally not active.
– Might miss genes that depend additional factors
other than Sigma-H.
• Identify potential promoters using computer
searches.
Pspac
sigH
∆sigH
wild-type
Grow cells
Isolate RNA
Make labeled cDNA
Mix and hybridize
Scan slide
Analyze data
wild type (Cy5) vs. sigH mutant (Cy3)
Hour -1
Hour 0
citG
Hour +1
sacT
Identifying differentially
expressed genes
• Many different methods
• Arbritrary assignment of fold change is not
a valid approach
• Statistical representation of the data
– Iterative outlier analysis
– SAM (significance analysis of microarrays)
Data from a microarray are
expressed as ratios
• Cy3/Cy5 or Cy5/Cy3
• Measuring differences in two samples, not
absolute expression levels
• Ratios are often log2 transformed before
analysis
Genes whose transcription is
influenced by sH
• 433 genes were altered when comparing wt
vs. ∆sigH.
• 160 genes were altered when sigH
overexpressed.
• Which genes are directly regulated by
Sigma-H?
Identifying sigH promoters
• Two bioinformatics approaches
– Hidden Markov Model database (P. Fawcett)
• HMMER 2.2 (hmm.wustl.edu)
– Pattern searches (SubtiList)
• Identify 100s of potential promoters
Correlate potential sigH
promoters with genes identified
with microarray data.
• Genes positively regulated by Sigma-H in a
microarray experiment that have a putative
promoter within 500bp of the gene.
Directly controlled sigH genes
• 26 new sigH promoters controlling 54 genes
• Genes involved in key processes associated with
the transition to stationary phase
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–
–
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generation of new food sources (ie. proteases)
transport of nutrients
cell wall metabolism
cyctochrome biogenesis
• Correctly identified nearly all known sigH
promoters
• Complete sigH regulon:
– 49 promoters controlling 87 genes.
• Identification of DNA
regions bound by
proteins.
Iyer et al. 2001 Nature, 409:533-538
Pathogen 1
Pathogen 2
Grow cells
Isolate RNA
Make labeled cDNA
Mix and hybridize
Scan slide
Analyze data