scholarly journals Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Cell Systems ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 192-205.e3 ◽  
Author(s):  
Brandon Ho ◽  
Anastasia Baryshnikova ◽  
Grant W. Brown
1996 ◽  
Vol 16 (12) ◽  
pp. 6794-6803 ◽  
Author(s):  
K Levine ◽  
K Huang ◽  
F R Cross

The three budding yeast CLN genes appear to be functionally redundant for cell cycle Start: any single CLN gene is sufficient to promote Start, while the cln1 cln2 cln3 triple mutant is Start defective and inviable. Both quantitative and apparently qualitative differences between CLN genes have been reported, but available data do not in general allow distinction between qualitative functional differences as opposed to simply quantitative differences in expression or function. To determine if there are intrinsic qualitative differences between Cln proteins, we compared CLN2, CLN3, and crippled (but still partially active) CLN2 genes in a range of assays that differentiate genetically between CLN2 and CLN3. The results suggest that different potencies of Cln2, Cln3, and Cln2 mutants in functional assays cannot be accounted for by a simple quantitative model for their action, since Cln3 is at least as active as Cln2 and much more active than the Cln2 mutants in driving Swi4/Swi6 cell cycle box (SCB)-regulated transcription and cell cycle initiation in cln1 cln2 cln3 bck2 strains, but Cln3 has little or no activity in other assays in which Cln2 and the Cln2 mutants function. Differences in Cln protein abundance are unlikely to account for these results. Cln3-associated kinase is therefore likely to have an intrinsic in vivo substrate specificity distinct from that of Cln2-associated kinase, despite their functional redundancy. Consistent with the idea that Cln3 may be the primary transcriptional activator of CLN1, CLN2, and other genes, the activation of CLN2 transcription was found to be sensitive to the gene dosage of CLN3 but not to the gene dosage of CLN2.


2021 ◽  
Author(s):  
◽  
Hannah D. Hoang

<p>The goal of this research was to use two-dimensional electrophoresis to examine changes in abundance of enzymes of the glycolytic pathway in the yeast Saccharomyces cerevisiae grown on carbon sources that support either fermentation to ethanol or oxidative metabolism. Large-scale profiling of protein abundances (expression proteomics) often detects changes in protein abundance between physiological states. Such changes in enzyme abundance are often interpreted as evidence of metabolic change although most textbooks emphasise control of enzyme activities not enzyme amount. Two-dimensional difference gel electrophoresis (2DDIGE) was therefore used to examine differences in protein abundance between S. cerevisiae strain BY4741 grown on either glucose (fermentation) or glycerol. Growth on 2% glucose, but not on glycerol, was accompanied by extensive production of ethanol. Doubling times for growth were 2 h 5 min in glucose and 9 h 41 min in glycerol. Conditions for extraction and two-dimensional electrophoresis of proteins were established. One hundred and seventy nine proteins were identified by MALDI mass spectrometry of tryptic digests of protein spots excised from Coomassie stained gels. All of the enzymes for conversion of glucose to ethanol, except for the second enzyme of glycolysis phosphoglucose isomerase, were identified using twodimensional electrophoresis of 100 μg of protein from cells grown on 2% glucose. Identification of proteins excised from the DIGE gels was more challenging, partly because of the lower amount of protein. Eight of the proteins that showed statistically significant differences in abundance (≥ 2-fold, p ≤ 0.01) between glucose and glycerol were identified by mass spectrometry of proteins excised from the 2DDIGE gels, and a further 18 varying proteins were matched to proteins identified from the Coomassie stained gels. Of these total 26 identified or matched proteins, subunits of five of the enzymes for conversion of glucose to ethanol were more abundant from the fermentative cells grown on glucose. The more abundant glycolytic enzymes were phosphofructokinase 2, fructose-1,6-bisphosphate aldolase, triosephosphate isomerase and enolase, plus pyruvate decarboxylase that was required for conversion of the glycolytic product pyruvate to acetaldehyde. The alcohol dehydrogenases Adh1 and Adh4 that convert acetaldehyde to ethanol were detected but did not vary significantly between growth on glucose or glycerol. The results confirmed that in this case changes in abundance of some enzymes were consistent with the altered metabolic output. Future studies should examine whether changes in the abundance and activity of these enzymes are responsible for the differences in metabolism.</p>


2019 ◽  
Author(s):  
Shu-Ting You ◽  
Yu-Ting Jhou ◽  
Cheng-Fu Kao ◽  
Jun-Yi Leu

ABSTRACTCell-to-cell heterogeneity within an isogenic population has been observed in prokaryotic and eukaryotic cells. Such heterogeneity often manifests at the level of individual protein abundance and may have evolutionary benefits, especially for organisms in fluctuating environments. Although general features and the origins of cellular noise have been revealed, details of the molecular pathways underlying noise regulation remain elusive. Here, we used experimental evolution of Saccharomyces cerevisiae to select for mutations that increase reporter protein noise. By combining bulk segregant analysis and CRISPR/Cas9-based reconstitution, we identified the methyltransferase Hmt1 as a general regulator of noise buffering. Hmt1 methylation activity is critical for the evolved phenotype, and noise buffering is primarily achieved via two Hmt1 methylation targets. Hmt1 functions as an environmental sensor to adjust noise levels in response to environmental cues. Moreover, Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast species, suggesting broad significance of noise regulation.Author SummaryCell-to-cell heterogeneity within an isogenic population has been observed in prokaryotic and eukaryotic cells. Such heterogeneity often manifests at the level of individual protein abundance and may have evolutionary benefits, especially for organisms in fluctuating environments. Here, we used experimental evolution of Saccharomyces cerevisiae to select for mutations that increase reporter protein noise and identified the methyltransferase Hmt1 as a general regulator of noise buffering. Hmt1 is a central hub protein that is involved in multiple basic cellular pathways, including chromatin remodeling/transcription, translation, ribosome biogenesis, and post-transcriptional regulation. Our results show that Hmt1 constrains the noise level of multiple cellular pathways under normal conditions, so the physiology of individual cells in a population will not deviate too much from optimal peak fitness. However, when cells encounter environmental stresses, HMT1 is quickly down-regulated and expression noise is enhanced to increase the likelihood of population survival. Moreover, the noise buffering function of Hmt1 is conserved in Schizosaccharomyces pombe that diverged from the common ancestor of Saccharomyces cerevisiae more than 400 million years ago. Since the Hmt1 network is conserved from yeast cells to human, it is quite possible that Hmt1-mediated noise buffering also operates in multicellular organisms.


2021 ◽  
Author(s):  
◽  
Hannah D. Hoang

<p>The goal of this research was to use two-dimensional electrophoresis to examine changes in abundance of enzymes of the glycolytic pathway in the yeast Saccharomyces cerevisiae grown on carbon sources that support either fermentation to ethanol or oxidative metabolism. Large-scale profiling of protein abundances (expression proteomics) often detects changes in protein abundance between physiological states. Such changes in enzyme abundance are often interpreted as evidence of metabolic change although most textbooks emphasise control of enzyme activities not enzyme amount. Two-dimensional difference gel electrophoresis (2DDIGE) was therefore used to examine differences in protein abundance between S. cerevisiae strain BY4741 grown on either glucose (fermentation) or glycerol. Growth on 2% glucose, but not on glycerol, was accompanied by extensive production of ethanol. Doubling times for growth were 2 h 5 min in glucose and 9 h 41 min in glycerol. Conditions for extraction and two-dimensional electrophoresis of proteins were established. One hundred and seventy nine proteins were identified by MALDI mass spectrometry of tryptic digests of protein spots excised from Coomassie stained gels. All of the enzymes for conversion of glucose to ethanol, except for the second enzyme of glycolysis phosphoglucose isomerase, were identified using twodimensional electrophoresis of 100 μg of protein from cells grown on 2% glucose. Identification of proteins excised from the DIGE gels was more challenging, partly because of the lower amount of protein. Eight of the proteins that showed statistically significant differences in abundance (≥ 2-fold, p ≤ 0.01) between glucose and glycerol were identified by mass spectrometry of proteins excised from the 2DDIGE gels, and a further 18 varying proteins were matched to proteins identified from the Coomassie stained gels. Of these total 26 identified or matched proteins, subunits of five of the enzymes for conversion of glucose to ethanol were more abundant from the fermentative cells grown on glucose. The more abundant glycolytic enzymes were phosphofructokinase 2, fructose-1,6-bisphosphate aldolase, triosephosphate isomerase and enolase, plus pyruvate decarboxylase that was required for conversion of the glycolytic product pyruvate to acetaldehyde. The alcohol dehydrogenases Adh1 and Adh4 that convert acetaldehyde to ethanol were detected but did not vary significantly between growth on glucose or glycerol. The results confirmed that in this case changes in abundance of some enzymes were consistent with the altered metabolic output. Future studies should examine whether changes in the abundance and activity of these enzymes are responsible for the differences in metabolism.</p>


2017 ◽  
Vol 4 (5) ◽  
pp. 169-174 ◽  
Author(s):  
Margarita Cabrera ◽  
Daniele Novarina ◽  
Irina L. Rempel ◽  
Liesbeth M. Veenhoff ◽  
Michael Chang

2005 ◽  
Vol 173 (4S) ◽  
pp. 284-284
Author(s):  
Carol A. Podlasek ◽  
Douglas Wood ◽  
Yi Tang ◽  
Kevin E. McKenna ◽  
Kevin T. McVary

2001 ◽  
Vol 36 (2) ◽  
pp. 196-201 ◽  
Author(s):  
F. Seibold ◽  
O. Stich ◽  
R. Hufnagl ◽  
S. Kamil ◽  
M. Scheurlen

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