Gene deletion strains of yeast were grown up in media, and the media was taken out at specific time points for mass-spec analysis. By measuring extracellular metabolites (footprinting), they were able to more quickly and easily get an idea on cellular metabolism. The authors then used discriminate function analysis and principal components analysis to map out the metabolomes of their strains and discover grouping according to gene-deletion. This was an effective way to classify unknown gene deletions, observing through ‘guilt-by-association’ which groups they fell in and thus what the gene most likely encoded.
Some side notes:
-Fingerprinting: measuring intracellular metabolites
-Footprinting: measuring extracellular metabolites
-“Yet the metabolome…should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism.”
-Marked changes in metabolome between log-phase and stationary phase growth
-Discriminant Functional Analysis: Using multiple variables to determine group membership. Usually trained on a set where both membership and variables are known, then used on samples where only variables are known to derive membership.
-Mutants could be distinguished based on footprinting
I found this to be simple, understandable high-throughput approach, but with some serious failings. It is easy to conceive of a gene-deletion having a downstream metabolic effect, classifying it by such an effect may mask it's true purpose. Proteins that are involved in the same functional pathway, yet at grossly different points, may be grouped falsely; though they would at least be correctly assigned to the same function. As a springboard toward verification and a method to narrow down candidate genes, this database will have its uses.
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