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Genome of the week Deinococcus radiodurans
• Highly resistant to DNA damage
– Most radiation resistant organism known
• Multiple genetic elements
– 2 chromosomes, 2 plasmids
– Why call one a chromosome vs. plasmid?
Why sequence D. radiodurans?
• Learn how this bacterium is so resistant to DNA damage
– This bacterium has nearly all known mechanisms for repairing
DNA damage.
– Redundancy of some DNA damage repair mechanisms.
• Use this organism in bioremediation.
– Sites contaminated with high levels of radioactivity
– DOE (Department of Energy) sequences many microbial genomes
- JGI
Data normalization
• Why do we need to normalize microarray data?
– Correct for experimental errors
• Northern blot example
• Microbial microarrays
– Assume the expression of most genes don’t change
– We know every gene - sum the intensity in both
channels and make the equal.
– Many other ways of normalizing data - not one standard
way. Area of active research.
Data Distribution Before and After Normalization
1200
cy3
cy5
1000
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Number of clones
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200
0
1400
cy3
cy5
1200
1000
800
600
400
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0
Log of Intensities
Experimental design
• Very important - often overlooked.
• Bacteria are easier to work with than more
complex systems.
• Two types we will discuss in broad terms:
– Direct comparison
– Reference design
– Also loop design (ANOVA)
Yang and Speed, 2002
Direct comparison
• Directly comparing all samples against each other.
• Best choice - lowest amount of variation in the
experiment.
• Not the best design
– Many samples are to be compared.
– RNA is not easy to obtain (often not a problem for
microbial systems.
– If microarrays are limiting.
Reference design (indirect)
• Compare all samples to a common reference.
– Usually a pool of all samples of RNA or genomic DNA
• Useful in comparing many samples.
• Drawbacks:
– 1/2 of the measurements are not biologically relevant
– Each gene is expressed as a ratio/ratio. Variation in the
ratios will be higher.
More complicated situations
• Multifactorial designs
Examples of applications
• Gene expression
– Defining a regulon - targets of a transcription
factor.
– Functional annotation
• Identifying regions of DNA bound by a
DNA binding protein
• Genome content
• Disease diagnosis
Characterization of the stationary
phase sigma factor regulon (sH)
in Bacillus subtilis
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
• Many known sigH promoters previously
identified
– Array validation
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
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
• 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
–
–
–
–
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