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Genome of the week - Deinococcus radiodurans

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 ?.

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Genome of the week - Deinococcus radiodurans

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  1. 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?

  2. 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

  3. 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.

  4. 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

  5. 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)

  6. Yang and Speed, 2002

  7. 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.

  8. 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.

  9. More complicated situations • Multifactorial designs

  10. 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

  11. Characterization of the stationary phase sigma factor regulon (sH) in Bacillus subtilis

  12. 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

  13. Alternative sigma factors in B. subtilis sporulation Kroos and Yu, 2000

  14. 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

  15. 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.

  16. ∆sigH wild-type

  17. sacT citG wild type (Cy5) vs. sigH mutant (Cy3) Hour -1 Hour 0 Hour +1

  18. 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

  19. 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?

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

  21. 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.

  22. 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.

  23. Identification of DNA regions bound by proteins. Iyer et al. 2001 Nature, 409:533-538

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