Upload
justina-burke
View
214
Download
1
Tags:
Embed Size (px)
Citation preview
Uses of Microarrays in Research
Anne RosenwaldBiology DepartmentGeorgetown University
Microarrays in Research:A Survey of PubMed
Schena, Shalon, Davis, and Brown (1995) Science 270, 467
Differential expression of 45 Arabidopsis genes!
0
500
1000
1500
2000
2500
3000
3500
4000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Nu
mb
er o
f P
ub
Med
Cit
atio
ns
Microarray
Protein Array
Recent Microarray Papers: I. New Techniques/Applications
5,000 RNAi experiments on a chip Lehner and Fraser (2004) Nat Methods 1, 103
RNA living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells Wheeler et al. (2004) Nat Methods 1, 127
Spots on chip contain dsRNA Chip incubated with Drosophila cells Cells induced to “take-up” RNA
Are cells alive or dead? Do cells have phosphorylated Akt? Do cells have altered actin fibrils?
Recent Microarray Papers: I. New Techniques/Applications
Transcriptional regulatory networks in Saccharomyces cerevisiae Lee et al. (2002) Science 298 799-804 “ChIP-on-chip”
Recent Microarray Papers: II. Improved Methods for Analysis/Access
Reproducibility and statistical rigor outbred organisms (i.e. humans) do different platforms give the same answers?
Tools for analysis Tools for access and annotation
an example based on Affymetrix chips GeneCruiser: a web service for the annotation
of microarray data Liefeld et al. Bioinformatics (2005) Jul 19 [epub]
can incorporate GO terms and link info with SwissProt, RefSeq, LocusLink, etc.
Primarily for mouse and human data
Mutational change: compare “wild type” to mutant Tissue-specific gene expression Environmental change: compare same organism in two
different environments Development: compare different stages along a
particular lineage Therapeutics: compare in cells/tissues treated with and
without the drug of interest Investigate changes in gene copy number Cancer: compare tumor with normal surrounding tissue
2005 papers with term “microarray” = 2450 Of those, also with term “cancer” = 624 (25%)
Recent Microarray Papers: III. Scientific Endeavors
Recent Microarray Papers: III. New Scientific Endeavors
Transgenic C. elegans as a model in Alzheimer's research Curr Alzheimer Res. 2005 Jan;2(1):37-45.
Compared wild type worms with worms expressing human A
Behavior and the limits of genomic plasticity: power and replicability in microarray analysis of honeybee brains Genes Brain Behav. 2005 Jun;4(4):267-71
Compared bees with long-standing behavioral differences (nursers v. foragers)
Compared recently hatched bees beginning to express behavioral differences (nursers v. foragers v. gravetenders)
Some basic yeast biology
Yeast come in two mating types MATa MAT
Can live either as haploids or as diploids diploids referred to as MATa/
Haploids of opposite mating type can mate to form new diploids
Diploids can be induced to undergo meiosis (“sporulation”) to make new haploids
General website for Saccharomyces (SGD) http://www.yeastgenome.org/
Materials available ~5500 genes cloned with tags for purification TAP-tagged fusion collections HA-tagged fusion collections GFP-tagged fusion collections Insertional mutant collections Knockout collections
Most of these available from OpenBiosystems www.openbiosystems.com
Yeast resources
The yeast knockout collection
Yeast knockout resources MATa/ heterozygous diploids (entire genome) MATa haploids (non-essentials) MAT haploids (non-essentials) MATa/ homozygous diploids (non-essentials)*
Yeast knockout website http://www-sequence.stanford.edu/group/
yeast_deletion_project/deletions3.html
*I have this collection, so if there’s a mutant you want, let me know.
The yeast knockout collection
http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html
Using the knockouts for microarrays
A Robust Toolkit for Functional Profiling of the Yeast Genome Pan et al. (2004) Mol Cell 16, 487
Takes advantage of the MATa/ heterozygous diploid collection identifies synthetic lethal interactions via diploid-
based synthetic lethality analysis by microarrays (“dSLAM”)
Uses dSLAM to identify those strains that upon knockout of a query gene, show growth defects synthetic lethal (the new double mutant = dead) synthetic fitness (the new double mutant = slow
growth)
Step 1: Creating the haploid convertible heterozygotes
Important point:This HIS3 gene is only expressed in MATa haploids, not in MAThaploids or MATa/ diploids
So in other words, can select against MATa/ diploids to ensure you’re looking at only haploids later on.
Step 2: Inserting the query mutation
Knockout one copy of your gene of interest (“Your Favorite Gene”) with URA3
Step 3: Make new haploids and select for strains of interest
Sporulate to get new haploids
Select on –his medium to ensure only haploids survive (no diploids)
selects against query mutation so genotype is xxx::KanMX YFG1
selects for query mutation so genotype is xxx::KanMX yfg1::URA3
Reminder about YKO construction
U1 D1
U2 D2
Using common oligos U1 and U2 (or D1 and D2) amplifies the UPTAG (or DNTAG) sequence unique to each of the KOs
Step 4: Prepare genomic DNA and do PCR with common TAG sequences
Step 4: Prepare genomic DNA and do PCR with common TAG sequences
The two different conditions are labeled with two different colors**
The labeled DNA is then incubated with a TAG microarray
**The PCR reactions create a mixture of TAGs (representing all the strains in the pool), since each KO has a unique set of identifier tags (UPTAG and DNTAG) bounded by common oligonucleotides
Evidence this really works – part I
Strains
x-axis y-axis
XXX/xxx::KanMXCAN1/CAN1
XXX/xxx::KanMXCAN1/can1::MFA1pr-HIS3
On average, the intensity is the same before and after 1 copy of the CAN1 gene is knocked out
Evidence this really works – part II
Strainsx-axis y-axis
DIPLOIDSXXX/xxx::KanMX
CAN1/can1::MFA1pr-HIS3
HAPLOIDSXXX or xxx::KanMXcan1::MFA1pr-HIS3
Red spots illustrate that fraction of the strains with KOs in essential genes, so when haploid, not present in pool
Another variation: Drug sensitivity
Another variation: Drug sensitivity
Summary
If you can compare two different conditions and you have a way to stick things to slides, some sort of microarray is possible!