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YGMB 2010

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Poster Session Print

Personal Robotics for Omics with High-Density Yeast Arrays

 Charles Boone1, Amy Tong1, Pawel Kusan2, Harry Singer3 and Carl Singer3

1Boone Lab, University of Toronto, Toronto, Canada M56 1L6. 2Engineering Services Inc., Toronto, Canada, M5R 2J7. 3Singer Instrument Co. Ltd., Roadwater, Somerset, TA23 ORE, UK.

 This is an ongoing colaboration of the above interested parties and the information contained herein forms part of a recent poster session

Abstract
Our aim was to produce a relatively inexpensive and simple robotics system for manipulation of high-density yeast arrays. The goal was to create a system that would enable individual yeast labs to manipulate high-density yeast arrays rapidly and efficiently, allowing reagents sets such as deletion mutant collection and the complete set of cloned yeast genes to be utilised for large-scale two hybrid, synthetic genetic array, phenotypic and chemical-genetic analysis. Instead of standard lab velvets, the RoToR robot utilises plastic replica plating pads, facilitating the transfer and construction of yeast arrays containing 96, 384 and 1536 colonies per plate.

Examples of High-Density Yeast Arrays
figure1

Arrays may consist of yeast cells harbouring plasmids expressing fusion proteins such as the yeast two-hybrid activation-domain (AD)-ORF (1) and GST-ORF (2) fusion plasmids, chromosomally tagged genes such as green fluorescene protein (GFP) (3) and tandem affinity purification (TAP) (4), or gene deletion mutants (5), Tet-promoter mutants (6), and transposon mutants (7).

Yeast Two-Hybrid Array
figure2

A MATα “Bait” strain carrying an DNA-binding domain (DBD) fusion plasmid is crossed to the ordered array activation domain (AD)-ORF. The resulting diploids are selected using markers carried on the two-hybrid plasmids. Positive interactions are selected using reporters such as HIS3, ADE2 and lacZ genes, and can be readily identified by their positions on the array.

 Phenotypic and Chemical-Genetic Screens
figure3

The arrays of ~5,000 viable deletion mutants (xxxΔ), and Tet-regulated mutants (TetO-XXX), representing the complete set of non-essential and essential genes in the yeast genome, can be screened with libraries of chemicals or under various environmental conditions (8).

 

Mapping Synthetic Lethality using Synthetic Genetic Array (SGA) Analysis

A MATα strain carrying a query mutation linked to a dominant selectable marker (natR), and the MFA1pr-HIS3, can1Δ and lyp1Δ reporters is crossed to an ordered array of MATa viable deletion mutants, or Tet-regulated mutants, each carrying a gene deletion mutation linked to the kanR marker. The resulting heterozygous diploids are selected on medium containing clonNAT and G418, and transferred to medium with reduced levels of carbon and nitrogen to induce the formation of haploid meiotic spore progeny. Selective germination MATa meiotic progeny is facilitated by the MFA1pr-HIS3 reporter on medium lacking histidine and containing canavine and thialysine. Double mutants are selected on medium containing clonNAT and G418, and scored for fitness (9, 10).

figure4

SGA Mapping (SGAM)
figure5

SGA analysis can be used to map the location of a mutant suppressor allele (s), which suppresses the lethality of natR-marked gene deletion mutation (queryD::natR) by combining with ~5,000 viable kanR-marked gene deletion mutations (xxxD::kanR) through mating, meiotic recombination, and germination of haploid meiotic spore progeny. In deletion strains where wild-type allele (S) is tightly linked to the kanR-marked deletion, the low frequency of recombination between the suppressor allele (s) and the kanR-marked deletion limits the recovery of viable natR kanR double-deletion progeny (11).

Applications of SGA Analysis
figure6

A natR-marked query strain can be crossed to any ordered array of strains (e.g. an GAL-ORF over-expression array) providing systematic approaches to genetic suppression analysis, dosage lethality, dosage suppression or plasmid shuffling (12).

References

1. Uetz et. al. (2000) Nature 403, 623-627.
2. Zhu et. al. (2001) Science 293, 2101-2105.
3. Huh et. al. (2003) Nature 425, 686-691.
4. Gavin et. al. (2002) Nature 415, 141-147.
5. Winzeler et. al. (1999) Science 285, 901-906.
  Giaever et. al. (2002) Nature 418, 387-391.
6. Mnaimneh et. al. (2004) Cell 118, 31-44.
7. Ross-Macdonald et. al. (1999) Nature 402, 413-418.
8. Parsons et. al. (2004) Nature Biotech. 22, 62-69.
9. Tong et. al. (2001) Science 294, 2364-2368.
10. Tong et. al. (2004) Science 303, 808-813.
11. Jorgensen et. al. (2002) Genetics 162, 1091-1099.
12. Sopko et. al. unpublished data.

 
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