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Linking Post-Translational Modifications and Variation of Phenotypic Traits

2012; Elsevier BV; Volume: 12; Issue: 3 Linguagem: Inglês

10.1074/mcp.m112.024349

1535-9484

Warren Albertin, Philippe Marullo, Marina Bely, Michel Aigle, Aurélie Bourgais, Olivier Langella, Thierry Balliau, Didier Chevret, Benoît Valot, Telma da Silva, Christine Dillmann, Dominique de Vienne, Delphine Sicard,

Microbial Metabolic Engineering and Bioproduction

Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits. Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits. 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Their genetic and plastic variability together with their effects on the phenotype remain to be studied. The present work focuses on the genetic and plastic variability of enzyme and isoform abundances in yeast, and on the possible consequences of this variability on metabolic and "life-history" traits, i.e. traits characterizing the lifespan of the organism such as growth or survival. Quantitative proteomics based on two-dimensional electrophoresis (2-DE) 1The abbreviations used are:2-DEtwo-dimensional electrophoresisBREMbrewery mediumBAMbakery mediumWIMwinery mediumLDAlinear discriminant analysisAFalcoholic fermentationMSmass spectrometryMCAmetabolic control analysis. 1The abbreviations used are:2-DEtwo-dimensional electrophoresisBREMbrewery mediumBAMbakery mediumWIMwinery mediumLDAlinear discriminant analysisAFalcoholic fermentationMSmass spectrometryMCAmetabolic control analysis. is well adapted for this purpose, because the different isoforms of a protein often have different electrophoretic mobility, resulting in distinguishable spots. We applied quantitative proteomics to Saccharomyces cerevisiae alcoholic fermentation (AF), a central metabolic pathway exploited for millennia in three important human food-processes: beer and wine production (39Cavalieri D. McGovern P.E. Hartl D.L. Mortimer R. Polsinelli M. 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Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels.Proc. Natl. Acad. Sci. U.S.A. 1996; 93: 14440-14445Crossref PubMed Scopus (1298) Google Scholar, 47Fiévet J. Dillmann C. Lagniel G. Davanture M. Negroni L. Labarre J. de Vienne D. Assessing factors for reliable quantitative proteomics based on two-dimensional gel electrophoresis.Proteomics. 2004; 4: 1939-1949Crossref PubMed Scopus (35) Google Scholar). In a previous work, we showed that life-history traits (carrying capacity and cell size) and metabolic phenotypes (maximum CO2 flux, ethanol, acetate, and glycerol content) displayed large variation, with medium effects usually higher than the strain effects (48Albertin W. Marullo P. Aigle M. Dillmann C. de Vienne D. Bely M. Sicard D. Population size drives the industrial yeast alcoholic fermentation and is under genetic control.Appl. Environ. Microbiol. 2011; 77: 2772-2784Crossref PubMed Scopus (39) Google Scholar). On the other hand, trade-offs were found between metabolic and life-history traits (49Spor A. Wang S. Dillmann C. de Vienne D. Sicard D. "Ant" and "grasshopper" life-history strategies in Saccharomyces cerevisiae.PLoS ONE. 2008; 3: e1579Crossref PubMed Scopus (30) Google Scholar). A recent work showed that the expression variation of a few genes involved in the upper part of glycolysis could drive changes in life-history strategies (50Wang S. Spor A. Nidelet T. Montalent P. Dillmann C. de Vienne D. Sicard D. Switch between life history strategies due to changes in glycolytic enzyme gene dosage in Saccharomyces cerevisiae.Appl. Environ. Microbiol. 2011; 77: 452-459Crossref PubMed Scopus (12) Google Scholar), indicating that life-history traits might be under the control of some metabolic enzymes. two-dimensional electrophoresis brewery medium bakery medium winery medium linear discriminant analysis alcoholic fermentation mass spectrometry metabolic control analysis. two-dimensional electrophoresis brewery medium bakery medium winery medium linear discriminant analysis alcoholic fermentation mass spectrometry metabolic control analysis. All these observations prompted us to investigate the possible control of metabolic and life-history traits by a large panel of AF enzymes tested under various conditions. Our experimental design included nine food-processing strains grown in triplicate in three different synthetic media mimicking the dough/wort/grape must found in bakery, brewery, and enology, to: (1) quantify thoroughly the abundances of 18 AF enzymes and their isoforms in a sample of 27 medium x strain combinations; (2) compare the genetic and plastic variability of the enzymes and their isoforms; (3) search which enzymes or isoforms, if any, are related to CO2 flux, AF metabolite concentrations, and life-history traits, and thus may exert control over metabolism and life-history strategy. Our results highlight the preponderant role of post-translational modifications in the variation of metabolic phenotypes and life-history traits. A detailed Materials and Methods section is available as Supporting Information. Nine S. cerevisiae strains were used (supplemental Table S1), from enology (E1 to E4), brewery (B1 and B2), and distillery origins (D1 to D3). All strains were grown in triplicates in three synthetic fermentative media differing by their amount of sugar, nitrogen, pH, osmotic pressure, and anaerobic growth factors to reflect main changes of fermentation medium between brewery (BREM), bakery (BAM), and winery (WIM) contexts (supplemental Table S2). For each of the 81 fermentations (nine strains × three media × three repetitions), we measured the following metabolic and life-history traits: CO2 specific flux (the CO2 production rate per cell, g/h/cell), ethanol production (% vol/cell), acetic acid concentration (g/cell), glycerol concentration (g/cell), biomass (gcell), carrying capacity (K or maximum population size in cells/ml), and cell size (μm [diameter]). One sample per fermentation (81 fermentations) was harvested at comparable physiological stage (maximal CO2 production rate before nutriment starvation). One 2-DE gel per sample was run and stained with colloidal-blue, which offers a linear relationship between spot quantification and protein abundance (47Fiévet J. Dillmann C. Lagniel G. Davanture M. Negroni L. Labarre J. de Vienne D. Assessing factors for reliable quantitative proteomics based on two-dimensional gel electrophoresis.Proteomics. 2004; 4: 1939-1949Crossref PubMed Scopus (35) Google Scholar) and thus allows accurate comparison of spot abundance between and within 2-DE gels. Spots of interest were quantified using Progenesis software (Nonlinear Dynamics, Newcastle, UK) and identified using mass spectrometry (MS). Almost all enzymes involved in glycolysis and ethanol pathways were identified, or at least the major and most abundant isozymes in case of paralogous genes. The variation of each isoform or enzyme abundance (in the latter case the isoforms of the enzyme were summed) was investigated through a mixed ANOVA model: Z=μ+mediumi+strainj+blockk+positionl+batchm+medium*strainij+εijklm where Z is the variable, medium is the medium effect (i = 1, 2, 3), strain is the strain effect (j = 1, …, 9), block is the random block effect (effect of each weekly experimental repetition, k = 1, …, 11), position is the random position effect (bioreactor position, l = 1, …, 15), batch is the random 2-DE batch effect (m = 1, …, 6), medium * strain is the interaction effect between medium and strain factors, and ε is the residual error. For further analyses (hierarchical clustering, PCA, LDA, regression analysis, etc.), we used the mean abundances predicted by the ANOVA model, that is, corrected for the random effects (block, position, and batch effects). The final data set is available as supplementary Data set S4. Hierarchical clustering was made using R (Ward's clustering method and Euclidean distances). Proteomic-trait relationships were explored using multiple linear regression to find enzymes and isoforms whose abundance was significantly related to metabolic and life-history traits. The impact of human domestication was investigated using linear discriminant analysis (LDA) to discriminate beer, distillery, and wine strains using R. Discriminant isoforms were identified through stepwise variable selection and through the calculation of the « ability to separate » (AS) criterion. To explore the extent of phenotypic diversity of enzymes abundance in alcoholic fermentation pathway, we chose nine food-processing strains of S. cerevisiae (supplemental Table S1) from different food origins, and we performed anaerobic alcoholic fermentations in triplicate using three different synthetic media (supplemental Table S2) that mimicked the dough/wort/grape must found in bakery, brewery, and enology (48Albertin W. Marullo P. Aigle M. Dillmann C. de Vienne D. Bely M. Sicard D. Population size drives the industrial yeast alcoholic fermentation and is under genetic control.Appl. Environ. Microbiol. 2011; 77: 2772-2784Crossref PubMed Scopus (39) Google Scholar). For each of the 81 fermentations (9 strains × 3 media × 3 repetitions), cell samples for proteomics assays were harvested during the fermentations when the CO2 production rate per cell (the flux) was close to its maximum, so that the cells displayed comparable physiological stage. Using quantitative proteomics, we identified and quantified the relative abundance of 15 enzymes of glycolysis and ethanol pathways, one enzyme of acetate pathway and two enzymes of glycerol pathway (Fig. 1). Those 18 enzymes were representative of the alcoholic fermentation metabolic process and will be thereafter called the fermentation proteome. For most enzymes, several spots, corresponding to different post-translational forms (isoforms) were identified (Fig. 1), allowing subsequent analyses both at the enzyme level (sum of all isoforms for each enzyme) and at the post-translational modification level (individual isoforms). The few suspected allelic variants identified by 2-DE (shifting trains of spots, Fig. 1B) were confirmed by gene sequence (supplementary Information Data set S1). In these last cases, we compared isoforms having the same position within the train of spots (acidic, basic and intermediary isoforms) rather than co-located spots (see Materials and Methods in Supplementary Information). The mean coefficient of variation between biological triplicates for isoforms was 18.4%, which is low enough to accurately detect small abundance variations. Proteomic data were released in the PROTICdb database, a web-based application designed for large-scale proteomic programs to store and query data related to protein separation by 2-DE and protein identification by MS (http://moulon.inra.fr/protic/adaptalevure. See Supplementary Information for details). We first analyzed the different sources of variation for protein abundance at different levels: At the whole fermentation proteome level (sum of enzymes of glycolysis, ethanol, acetate, and glycerol pathways), at the enzymes level (sum of isoforms for each enzyme), and at the post-translational level using individual isoforms (Table I). Considered globally, the sum of the abundance of the enzymes involved in the fermentation proteome (42 isoforms) represents on average 32.87 ± 1.89% of the total analyzed proteome (2265 ± 209 spots depending on the 2-DE gel). Variance analysis (ANOVA) revealed that such fermentation pool displayed no medium, no strain, and no medium × strain interaction effects, indicating that the enzymatic pool allocated to glycolysis, ethanol, acetate, and glycerol pathways is invariant whatever the medium and strain considered. Within the fermentation proteome, the abundance from one enzyme to another (sum of all isoforms for each enzyme) varied greatly (supplemental Fig. S1), with highly abundant proteins (Tdh2p, Tdh3p, Eno2p, Fba1p) and poorly represented enzymes (PfK1p, Ald6p, Pgi1p, Hor2p, Rhr2p). However, although abundance had important variation within enzymes, among strains, the proportion allocated to each enzyme appeared to be globally conserved (Fig. 2). Indeed, the mean coefficients of variation of the 18 enzymes (CV = 0.26) and 42 isoforms (CV = 0.35) were significantly lower than the mean coefficient of variation of the 688 other common spots (non-AF proteins) on the 2-DE gels (CV = 1.24, Kolmogorov-Smirnov test, p value = 2.48 × 10−10 and 2.89 × 10−15, respectively). However, although the abundance of AF enzymes appeared more constrained than the whole proteome, significant variations were found, in particular for the enzymes of the last part of glycolysis (except Tdh3p and Gpm1p), as well as for the enzymes of ethanol, acetate, and glycerol pathways (Table I). A significant strain effect was found for most enzymes (13/18), which accounted for 21% to 68% of total variation (Table I). The medium effect was significant for only 6/18 enzymes and accounted for much less of the total variation (between 4 and 28%, Table I). The medium × strain interaction effect was significant for 2/18 enzymes, and accounted for 15% to 16% of total variation of the enzyme. Finally only 5/18 enzymes exhibited no strain or medium effect, and the average abundance of Pgi1p, Fba1p, and Tpi1p, corresponding to the first part of glycolysis, was similar in all the 27 medium × strain combinations. Therefore, we found a significant variation for enzyme abundance, which was better explained by