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Uses of Microarrays in Research Anne Rosenwald Biology Department Georgetown University.

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1 Uses of Microarrays in Research Anne Rosenwald Biology Department Georgetown University

2 Microarrays in Research: A Survey of PubMed Schena, Shalon, Davis, and Brown (1995) Science 270, 467 Differential expression of 45 Arabidopsis genes!

3 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?

4 Recent Microarray Papers: I. New Techniques/Applications  Transcriptional regulatory networks in Saccharomyces cerevisiae Lee et al. (2002) Science 298 799-804 “ChIP-on-chip”

5 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

6  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

7 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)

8 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

9  General website for Saccharomyces (SGD)  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 Yeast resources

10 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- /deletions3.html *I have this collection, so if there’s a mutant you want, let me know.

11 The yeast knockout collection

12 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)

13 Step 1: Creating the haploid convertible heterozygotes Important point: This HIS3 gene is only expressed in MATa haploids, not in MAThaploids or MATa/ diploids So in other words, can select against MATa/ diploids to ensure you’re looking at only haploids later on.

14 Step 2: Inserting the query mutation Knockout one copy of your gene of interest (“Your Favorite Gene”) with URA3

15 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

16 Reminder about YKO construction

17 U1D1 U2D2 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

18 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

19 Evidence this really works – part I Strains x-axisy-axis XXX/xxx::KanMX CAN1/CAN1 XXX/xxx::KanMX CAN1/can1::MFA1pr-HIS3 On average, the intensity is the same before and after 1 copy of the CAN1 gene is knocked out

20 Evidence this really works – part II Strains x-axisy-axis DIPLOIDS XXX/xxx::KanMX CAN1/can1::MFA1pr-HIS3 HAPLOIDS XXX or xxx::KanMX can1::MFA1pr-HIS3 Red spots illustrate that fraction of the strains with KOs in essential genes, so when haploid, not present in pool

21 Another variation: Drug sensitivity


23 Summary  If you can compare two different conditions and you have a way to stick things to slides, some sort of microarray is possible!

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