Genome of the week - Deinococcus radiodurans

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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?

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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

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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 dont 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.

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Data Distribution Before and After Normalization
1200
cy3
1000
cy5
800
600
400
200
0
2
5
8
11
3.5
6.5
9.5
2.75
4.25
5.75
7.25
8.75
10.3
Number of clones
1400
cy3
1200
cy5
1000
800
600
400
200
0
0
1
2
3
-3
-2
-1
0.5
1.5
2.5
-2.5
-1.5
-0.5
Log of Intensities
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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)

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Yang and Speed, 2002
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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.

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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.

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More complicated situations

• Multifactorial designs

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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

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Characterization of the stationary phase sigma
factor regulon (sH) in Bacillus subtilis
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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
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Alternative sigma factors in B. subtilis
sporulation
Kroos and Yu, 2000
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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

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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.

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?sigH
wild-type
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wild type (Cy5) vs. sigH mutant (Cy3)
Hour -1
Hour 0
Hour 1
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(No Transcript)
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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

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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?

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Identifying sigH promoters

• Two bioinformatics approaches
• Hidden Markov Model database
• HMMER 2.2 (hmm.wustl.edu)
• Pattern searches (SubtiList)
• Identify 100s of potential promoters

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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.

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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.

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• Identification of DNA regions bound by proteins.

Iyer et al. 2001 Nature, 409533-538