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Kyle A Serikawa

Publications and source records attributed to Kyle A Serikawa.

2 recordsLinked to original sources

Gene expression analyzed by high-resolution state array analysis and quantitative proteomics: response of yeast to mating pheromone.

The transcriptome provides the database from which a cell assembles its collection of proteins. Translation of individual mRNA species into their encoded proteins is regulated, producing discrepancies between mRNA and protein levels. Using a new modeling approach to data analysis, a striking diversity is revealed in association of the transcriptome with the translational machinery. Each mRNA has its own pattern of ribosome loading, a circumstance that provides an extraordinary dynamic range of regulation, above and beyond actual transcript levels. Using this approach together with quantitative proteomics, we explored the immediate changes in gene expression in response to activation of a mitogen-activated protein kinase pathway in yeast by mating pheromone. Interestingly, in 26% of those transcripts where the predicted protein synthesis rate changed by at least 3-fold, more than half of these changes resulted from altered translational efficiencies. These observations underscore that analysis of transcript level, albeit extremely important, is insufficient by itself to describe completely the phenotypes of cells under different conditions.

Computational Biology↗

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Cell Cycle↗