Complementary Profiling of Gene Expression at the Transcriptome and Proteome Levels in Saccharomyces cerevisiae*S

By T. Griffin, Steven P. Gygi, Trey Ideker, B. Rist, Jimmy K. Eng, Leroy Hood, R. Aebersold. Molecular & Cellular Proteomics, 2002

663 citations recorded by the source corpus.

Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2766c35ca909ecdecf955005505c51be235f77ab

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Citations in the corpus

Integrating high-throughput and computational data elucidates bacterial networks (2004, background): “Such behaviour, underscored by similar observations of mRNA transcript levels and corresponding protein product abundance in yeas”
Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. (2001, background): “Die Autoren haben in einer darauf folgenden Arbeit[6] ebenfalls solche Experimente durchgeführt und dort unter etwas veränderten äußeren Bedingungen eine ähnliche Korrelation festgestellt.”
Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S (2005, background): “within moderate to high abundance expression ranges (11).”
Variation in alternative splicing across human tissues (2004, background): “The relation between the amount of mRNA expressed from a gene and the concentration of the corresponding protein has been examined previously in several studies in yeast as well as in human and mouse liver tissues [55-58].”
RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts (2006, background): “It is believed, however, that the steady-state levels of mRNAs in a cell do not always directly correlate with the steady-state protein level”
Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. (2003, background): “…such noisy data with simple correlation analyses second liver sample (indicating good, although not perfect reproducibility) but averages only 79% for brain,have resulted in positive but weak associations (Griffin et al., 2002; Lian et al., 2001) between mRNA and protein, heart and kidney.”
1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data (2012, background): “Indeed, while early investigations found low or no correlation between proteome and transcriptome [6,7], recently much higher levels of global correlation have been reported [8,9].”
Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. (2005, result): “Thus, similar to studies done in yeast (Griffin et al., 2002; Washburn et al., 2003), bacteria (Baliga et al.”
Proteomics: the first decade and beyond (2003, background): “nor the response to perturbation-induced changes in mRNA or protein is mutually predictabl”
Estimating accuracy of RNA-Seq and microarrays with proteomics (2009, neutral): “5 [6,22-27], thus indicating that mRNA and protein expression levels are not fully independent.”
Correlation of mRNA and protein in complex biological samples (2009, neutral): “carbon source perturbations [42].”
Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents*S (2004, result): “In agreement with previous studies (34, 35), these measurements of protein and mRNA levels are revealing quite different and nonoverlapping aspects of the overall phenotypic changes induced by the deletion of these factors.”