Johns Hopkins researchers have built a powerful "toolkit" designed to quickly uncover how yeast's genes interact with each other.
Because 60 percent of yeast genes have at least one clearly identifiable human counterpart, the advance, described in the Nov. 5 issue of Molecular Cell, should speed advances in understanding human gene and protein functions, as well as improve the reliability of what scientists think they know about this extremely useful microorganism. Eventually the work with yeast could reveal particular gene interactions that could become targets for therapies to fight cancers or fungal infections, say the researchers.
The toolkit, a combination of techniques developed by the Hopkins researchers and others, starts with a collection of almost 6,000 yeast strains, each missing a different gene, and allows researchers to identify genes whose coupled elimination kills the yeast. Many laboratories are already using the "single knock-out" yeast collections, but postdoctoral fellow Xuewen Pan, Ph.D., found a way to protect the genetic integrity of the collection so that repeated experiments will provide the same results, regardless of when and where the experiments are conducted.
"Everyone in the yeast community has been using their own batch of yeast mutants, but the slow-growing mutants gradually accumulate extra genetic changes so they can grow faster," says Jef Boeke, Ph.D., professor of molecular biology and genetics and director of the HighThroughput Biology (HiT) Center in Hopkins' Institute for Basic Biomedical Sciences. "This potential for genetic impurity means that one person's batch of yeast is no longer exactly the same as someone else's. We went back to the original stocks of yeast mutants, in certain cases, so we know exactly what we have."
Human cells, with the exception of egg and sperm, have two copies of each gene, but yeast are content with either two copies of each gene or just one. Libraries of the almost 6,000 yeast mutants have just one copy of each gene, so there's no back-up for a missing gene that leads to slow growth.
Pan's mutant yeast are protected from collecting genetic impurities because he's added a second copy of all the genes, a cloak that temporarily obscures the effects of whatever gene is missing. He then uses a laboratory trick developed by researchers at the University of Toronto to get rid of the extra set of genes at just the right time.
A second advantage of the Hopkins "toolkit," Boeke says, is that all the yeast mutants are mixed together and studied simultaneously, an advance reported a year ago in Nature Genetics by then-graduate student Siew-Loon Ooi, Boeke, and Stanford University's Dan Shoemaker. At the end of an experiment, each mutant in the mix is identified by a genetic "barcode" -- created by Shoemaker -- embedded in its genome. The researchers then use special microarrays to find out how much of each mutant is present. An improved barcode microarray, designed by research associate Daniel Yuan, replaces the original in the new toolkit.
"Much like barcodes identify your purchases at the grocery store, these genetic barcodes identify each of the yeast mutants," says Boeke. "So we can mix the mutants together, challenge them to survive removal of a particular gene, nurture the ones that make it and use microarrays to see quickly which ones are missing."
The researchers dubbed the combined technique dSLAM, for diploid-based synthetic lethality analysis on microarrays. "Diploid" reflects the second set of genes added to the yeast mutants, and "synthetic lethality" refers to genes that only kill the yeast if missing in combination. dSLAM is easier to use than the earlier version, so it's more likely to be widely adopted by the yeast research community, Boeke says.
To test the new method, Pan and the team applied it to synthetic lethality experiments already tested by other methods. Their analysis, conducted with Forrest Spencer, Ph.D., an associate professor in the McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins, revealed that the new technique missed fewer of the known gene interactions and provided more consistent results than older techniques.
"No technique is going to give 100 percent, so the question becomes, How many can you miss and still be happy with the results?" says Boeke. "We think our numbers are sufficient to get the big picture of how genes interact, and the technique has better potential to scale to the whole genome than other techniques."
In one set of experiments, the new technique identified 116 genes that were synthetic lethal with a gene called cin8 and confirmed their involvement. Of these genes, 73 had not been identified by other techniques. The new technique missed just 16 genes previously identified and confirmed by the older techniques.
Boeke's goal is to use the new technique to build detailed maps of yeast genes' interactions, an ambitious project being done with Joel Bader, Ph.D., an assistant professor of biomedical engineering in the Whiting School of Engineering at Johns Hopkins and a computational biologist in the HiT Center, among others.
Authors on the report are Pan, Yuan, Bader, Spencer, Boeke, Xiaoling Wang and Sharon Sookhai-Mahadeo, of Johns Hopkins; Dong Xiang of the University of North Carolina at Chapel Hill; and Philip Hieter of the University of British Columbia, Vancouver. The research was funded by the Leukemia and Lymphoma Society, The Burroughs-Wellcome Program in Computational Biology at Johns Hopkins, a Kirschstein Fellowship from the National Institutes of Health, the Whitaker Foundation, the National Human Genome Research Institute and the National Cancer Institute.
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