Review of important points from the NCBI lectures.

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
Translated BLAST
ucleotide
rotein
Particularly useful for nucleotide sequences
without protein annotations, such as ESTs or
genomic DNA
Query
Database
Program
P
N
blastx
N
P
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 vs. glass slide based arrays

• 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

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

• Robert A. Britton and Alan D. Grossman -
  Massachusetts Institute of Technology.

• Patrick Eichenberger, Eduardo Gonzalez-Pastor,
  and Richard Losick - Harvard University.

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
wild type (Cy5) vs. sigH mutant (Cy3)
Hour -1
Hour 0
Hour 1
16
(No Transcript)
17
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)

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 (P. Fawcett)
• 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, 409533-538
24
Pathogen 1
Pathogen 2