The function of many of the uncharacterized open reading frames discovered by genomic sequencing can be determined at the level of expressed gene products, the proteome. However, identifying the cognate gene from minute amounts of protein has been one of the major problems in molecular biology. Using yeast as an example, we demonstrate here that mass spectrometric protein identification is a general solution to this problem given a completely sequenced genome. As a first screen, our strategy uses automated laser desorption ionization mass spectrometry of the peptide mixtures produced by in-gel tryptic digestion of a protein. Up to 90% of proteins are identified by searching sequence data bases by lists of peptide masses obtained with high accuracy. The remaining proteins are identified by partially sequencing several peptides of the unseparated mixture by nanoelectrospray tandem mass spectrometry followed by data base searching with multiple peptide sequence tags. In blind trials, the method led to unambiguous identification in all cases. In the largest individual protein identification project to date, a total of 150 gel spots-many of them at subpicomole amounts-were successfully analyzed, greatly enlarging a yeast two-dimensional gel data base. More than 32 proteins were novel and matched to previously uncharacterized open reading frames in the yeast genome. This study establishes that mass spectrometry provides the required throughput, the certainty of identification, and the general applicability to serve as the method of choice to connect genome and proteome.
In yeast, the transition between the fermentative and the oxidative metabolism, called the diauxic shift, is associated with major changes in gene expression and protein synthesis. The zinc cluster protein Cat8p is required for the derepression of nine genes under nonfermentative growth conditions (ACS1, FBP1, ICL1, IDP2, JEN1, MLS1, PCK1, SFC1, and SIP4). To investigate whether the transcriptional control mediated by Cat8p can be extended to other genes and whether this control is the main control for the changes in the synthesis of the respective proteins during the adaptation to growth on ethanol, we analyzed the transcriptome and the proteome of a cat8⌬ strain during the diauxic shift. In this report, we demonstrate that, in addition to the nine genes known as Cat8p-dependent, there are 25 other genes or open reading frames whose expression at the diauxic shift is altered in the absence of Cat8p. For all of the genes characterized here, the Cat8p-dependent control results in a parallel alteration in mRNA and protein synthesis. It appears that the biochemical functions of the proteins encoded by Cat8p-dependent genes are essentially related to the first steps of ethanol utilization, the glyoxylate cycle, and gluconeogenesis. Interestingly, no function involved in the tricarboxylic cycle and the oxidative phosphorylation seems to be controlled by Cat8p.
Hepatocellular carcinoma (HCC) is the major primary liver cancer. Glypican-3 (GPC3), one of the most abnormally expressed genes in HCC, participates in liver carcinogenesis. Based on data showing that GPC3 expression is posttranscriptionally altered in HCC cells compared to primary hepatocytes, we investigated the implication of microRNAs (miRNAs) in GPC3 overexpression and HCC. To identify GPC3-regulating miRNAs, we developed a dual-fluorescence FunREG (functional, integrated, and quantitative method to measure posttranscriptional regulations) system that allowed us to screen a library of 876 individual miRNAs. Expression of candidate miRNAs and that of GPC3 messenger RNA (mRNA) was measured in 21 nontumoral liver and 112 HCC samples. We then characterized the phenotypic consequences of modulating expression of one candidate miRNA in HuH7 cells and deciphered the molecular mechanism by which this miRNA controls the posttranscriptional regulation of GPC3. We identified five miRNAs targeting GPC3 3 0 -untranslated region (UTR) and regulating its expression about the 876 tested. Whereas miR-96 and its paralog miR-1271 repressed GPC3 expression, miR-129-1-3p, miR-1291, and miR-1303 had an inducible effect. We report that miR-1271 expression is down-regulated in HCC tumor samples and inversely correlates with GPC3 mRNA expression in a particular subgroup of HCC. We also report that miR-1271 inhibits the growth of HCC cells in a GPC3-dependent manner and induces cell death. Conclusion: Using a functional screen, we found that miR-96, miR-129-1-3p, miR-1271, miR-1291, and miR-1303 differentially control GPC3 expression in HCC cells. In a subgroup of HCC, the up-regulation of GPC3 was associated with a concomitant down-regulation of its repressor miR-1271. Therefore, we propose that GPC3 overexpression and its associated oncogenic effects are linked to the down-regulation of miR-1271 in HCC. (HEPATOLOGY 2013;57:195-204) H epatocellular carcinoma (HCC) is the most common form of primary liver cancer. 1 It usually develops in an affected liver with cirrhosis due to viral infection (hepatitis B virus, HBV; hepatitis C virus, HCV), alcohol abuse, metabolic disorders, or a carcinogenic agent. 1-3 HCC is a very heterogeneous class of tumors characterized by multiple types of genomic damages associated with its various
The regulation of glycolytic genes in response to carbon source in the yeast Saccharomyces cerevisiae has been studied. When the relative levels of each glycolytic mRNA were compared during exponential growth on glucose or lactate, the various glycolytic mRNAs were found to be induced to differing extents by glucose. No significant differences in the stabilities of the PFK2, PGKI, PYKI, or PDCI mRNAs during growth on glucose or lactate were observed. PYK::lacZ and PGK::lacZ fusions were integrated independently into the yeast genome at the ura3 locus. The manner in which these fusions were differentially regulated in response to carbon source was similar to that of their respective wild-type loci. Therefore, the regulation of glycolytic mRNA levels is mediated at the transcriptional level. When the mRNAs are ordered with respect to the glycolytic pathway, two peaks of maximal induction are observed at phosphofructokinase and pyruvate kinase. These enzymes (i) catalyze the two essentially irreversible steps on the pathway, (ii) are the two glycolytic enzymes that are circumvented during gluconeogenesis and hence are specific to glycolysis, and (iii) are encoded by mRNAs that we have shown previously to be coregulated at the translational level in S. cerevisiae (P. A. Moore, A. J. Bettany, and A. J. P. Brown, NATO ASI Ser. Ser. H Cell Biol. 49:421-432, 1990). This differential regulation of glycolytic mRNA levels might therefore have a significant influence upon glycolytic flux in S. cerevisiae.The glycolytic pathway plays a fundamental role in the provision of metabolic energy and intermediates during fermentative growth in the yeast Saccharomyces cerevisiae. Under these conditions, the glycolytic genes are among the most efficiently expressed genes in this organism, the glycolytic enzymes comprising over 30% of soluble cell protein (for reviews, see references 20 and 70). Most yeast glycolytic genes have been isolated and sequenced (2, 4, 11, 21, 23, 25, 27, 29-31, 40, 59, 62, 63, 68, 69). The high-level expression of yeast glycolytic genes is dependent upon complex interactions between a number of cis-acting promoter elements and trans-acting transcription factors which include the RAP1, ABF1, GCR1, and GAL11 proteins (3,8,10,12,13,15,41,48,55,60,61).It is not clear whether the expression of all glycolytic genes is induced when yeast cultures are transferred from nonfermentative to fermentative carbon sources. Maitra and Lobo (37) observed 3-to 100-fold increases in the levels of glycolytic enzymes following the addition of glucose to yeast cultures growing on acetate. This work was performed on a hybrid yeast strain generated by a cross between Saccharomyces fragilis and Saccharomyces dobzhanskii (37). Some more recent studies appear to confirm this observation for S. cerevisiae. For example, analyses of the enolase (EN02), phosphoglygerate kinase (PGKJ), pyruvate kinase (PYKI), pyruvate decarboxylase (PDCI), and alcohol dehydrogenase (ADHI) mRNAs have suggested that their levels are regulated in response to ...
In Saccharomyces cerevisiae the transition between the fermentative and the oxidative metabolism, called the diauxic shift, is associated with major changes in gene expression. In this study, we characterized a novel family of five genes whose expression is induced during the diauxic shift. These genes, FET3, FTR1, TIS11, SIT1, and FIT2, are involved in the iron uptake pathway. We showed that their induction at the diauxic shift is positively controlled by the Snf1/Snf4 kinase pathway. The transcriptional factor Aft1p, which is known to control their induction in response to iron limitation, is also required for their induction during the diauxic shift. The increase of the extracellular iron concentration does not affect this induction, indicating that glucose exhaustion by itself would be the signal. The possibility that the Snf1/Snf4 pathway was also involved in the induction of the same set of genes in response to iron starvation was considered. We demonstrate here that this is not the case. Thus, the two signals, glucose exhaustion and iron starvation, use two independent pathways to activate the same set of genes through the Aft1p transcriptional factor.
In the past 10 years, transcriptome and proteome analyses have provided valuable data on global gene expression and cell functional networks. However, when integrated, these analyses revealed partial correlations between mRNA expression levels and protein abundance thus suggesting that post-transcriptional regulations may be in part responsible for this discrepancy. In the present work, we report the development of a functional, integrated, and quantitative method to measure post-transcriptional regulations that we named FunREG. This method enables (i) quantitative measure of post-transcriptional regulations mediated by selected 3-untranslated regions and exogenous small interfering-RNA or micro-RNAs and (ii) comparison of these regulatory processes in physiologically relevant systems (e.g. cancer versus primary untransformed cells). We applied FunREG to the study of liver cancer, and we demonstrate for the first time the differential regulatory mechanisms controlling gene expression at a post-transcriptional level in normal and tumoral hepatic cells. As an example, translation efficiency mediated by heparin-binding epidermal growth factor 3-untranslated region was increased 3-fold in liver cancer cells compared with normal hepatocytes, whereas stability of an mRNA containing a portion of Cyclin D1 3-untranslated region was increased more than 2-fold in HepG2 cells compared with normal hepatocytes. Consequently we believe that the method presented herein may become an important tool in fundamental and medical research. This approach is convenient and easy to perform, accessible to any investigator, and should be adaptable to a large number of cell type, functional and chemical screens, as well as genome scale analyses. Finally FunREG may represent a helpful tool to reconcile transcriptome and proteome data.
With the systematic sequencing of the yeast genome, yeast biology has entered a new era where novel challenges have to be faced. One challenge is the identification of the function of the several hundred novel genes discovered by genome sequencing. Another is to understand how all yeast genes act in concert to ensure and maintain cell organization. Two-dimensional (2-D) gel electrophoresis is the technique of choice to take up these challenges because it provides the opportunity of obtaining an overall view of genome expression. In prospect of these studies we have undertaken the construction of a yeast 2-D gel protein database that contains information on polypeptides of the yeast protein map. In this paper we report the information presently contained in this database. The reported information includes the identification of 250 protein spots and the characterization of polypeptides corresponding to N-terminal acetylated proteins, mitochondrial proteins, glucose-repressed proteins, heat shock induced proteins and proteins encoded by intron-containing genes. In all, 600 spots are annotated. These data can be accessed on the Yeast Protein Map server through the World Wide Web network.
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