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Review of important points from the NCBI lectures. Example slides

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.

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Review of important points from the NCBI lectures. Example slides

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  1. 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

  2. Web Access Text Entrez Sequence BLAST Structure VAST

  3. P P P P P P P P P P P P N P P P P P P P P P P P P P Translated BLAST ucleotide rotein Particularly useful for nucleotide sequences without protein annotations, such as ESTs or genomic DNA Program Query Database P N blastx P N tblastn N N tblastx

  4. Position Specific Score Matrix (PSSM) A R N D C Q E G H I L K M F P S T W Y V 206 D 0 -2 0 2 -4 2 4 -4 -3 -5 -4 0 -2 -6 1 0 -1 -6 -4 -1 207 G -2 -1 0 -2 -4 -3 -3 6 -4 -5 -5 0 -2 -3 -2 -2 -1 0 -6 -5 208 V -1 1 -3 -3 -5 -1 -2 6 -1 -4 -5 1 -5 -6 -4 0 -2 -6 -4 -2 209 I -3 3 -3 -4 -6 0 -1 -4 -1 2 -4 6 -2 -5 -5 -3 0 -1 -4 0 210 S -2 -5 0 8 -5 -3 -2 -1 -4 -7 -6 -4 -6 -7 -5 1 -3 -7 -5 -6 211 S 4 -4 -4 -4 -4 -1 -4 -2 -3 -3 -5 -4 -4 -5 -1 4 3 -6 -5 -3 212 C -4 -7 -6 -7 12 -7 -7 -5 -6 -5 -5 -7 -5 0 -7 -4 -4 -5 0 -4 213 N -2 0 2 -1 -6 7 0 -2 0 -6 -4 2 0 -2 -5 -1 -3 -3 -4 -3 214 G -2 -3 -3 -4 -4 -4 -5 7 -4 -7 -7 -5 -4 -4 -6 -3 -5 -6 -6 -6 215 D -5 -5 -2 9 -7 -4 -1 -5 -5 -7 -7 -4 -7 -7 -5 -4 -4 -8 -7 -7 216 S -2 -4 -2 -4 -4 -3 -3 -3 -4 -6 -6 -3 -5 -6 -4 7 -2 -6 -5 -5 217 G -3 -6 -4 -5 -6 -5 -6 8 -6 -8 -7 -5 -6 -7 -6 -4 -5 -6 -7 -7 218 G -3 -6 -4 -5 -6 -5 -6 8 -6 -7 -7 -5 -6 -7 -6 -2 -4 -6 -7 -7 219 P -2 -6 -6 -5 -6 -5 -5 -6 -6 -6 -7 -4 -6 -7 9 -4 -4 -7 -7 -6 220 L -4 -6 -7 -7 -5 -5 -6 -7 0 -1 6 -6 1 0 -6 -6 -5 -5 -4 0 221 N -1 -6 0 -6 -4 -4 -6 -6 -1 3 0 -5 4 -3 -6 -2 -1 -6 -1 6 222 C 0 -4 -5 -5 10 -2 -5 -5 1 -1 -1 -5 0 -1 -4 -1 0 -5 0 0 223 Q 0 1 4 2 -5 2 0 0 0 -4 -2 1 0 0 0 -1 -1 -3 -3 -4 224 A -1 -1 1 3 -4 -1 1 4 -3 -4 -3 -1 -2 -2 -3 0 -2 -2 -2 -3 Serine is scored differently in these two positions Active site nucleophile

  5. PSI-BLAST Create your own PSSM: Confirming relationships of purine nucleotide metabolism proteins BLOSUM62 PSSM query Alignment Alignment

  6. 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 Affymetrix vs. glass slide based arrays

  7. Bacterial DNA microarrays • Small genome size • Fully sequenced genomes, well annotated • Ease of producing biological replicates • Genetics

  8. Applications of DNA microarrays • Monitor gene expression • Study regulatory networks • Drug discovery - mechanism of action • Diagnostics - tumor diagnosis • etc. • Genomic DNA hybridizations • Explore microbial diversity • Whole genome comparisons • Diagnostics - tumor diagnosis • ?

  9. 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.

  10. 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

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

  12. 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: • phosphorelay (kinA, spo0F) • sporulation (sigF, spoVG) • cell division (ftsAZ) • cell wall (dacC) • general metabolism (citG) • phosphatase inhibitors (phr peptides)

  13. 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.

  14. ∆sigH wild-type

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

  16. 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)

  17. 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

  18. 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?

  19. 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

  20. 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.

  21. 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.

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

  23. Pathogen 1 Pathogen 2

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