Search

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image‐based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single‐cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome‐wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell‐based screens at this depth reveals widespread RNAi‐induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell‐to‐cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large‐scale RNAi screens are increasingly performed to reach a systems‐level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single‐cell microenvironment., Molecular Systems Biology, 8 (1), ISSN:1744-4292

With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states. The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition. For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome., The metabolic network in the yeast Saccharomyces cerevisiae has been very well characterized in terms of components and topology. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. In this study, we exploited quantitative proteomic assays based on selected reaction monitoring (SRM) mass spectrometry to comprehensively analyze the set of enzymes involved in carbon and amino-acid metabolism in yeast (Figure 1), throughout a set of different metabolic states. To elucidate how this metabolic network of proteins adapts to environmental challenges, we chose five nutritional conditions resulting in maximal difference in magnitude and direction of metabolic fluxes. We could reproducibly detect and quantify across the different conditions, 90% of the targeted metabolic proteome, including complete families of isoenzymes, sharing up to 99.5% sequence identity and multi-subunit enzyme complexes. This yielded an information-rich proteomic data set that represents a nutritionally perturbed biological system with high coverage of its components. Interpreted through flux balance modeling, the data indicate that S. cerevisiae expresses—at least at a basal level—more proteins than are actually necessary for sustaining a given metabolic condition. One potential explanation for the presence of non-necessary proteins is that these enzymes could realize immediate basal metabolic fluxes upon a change to new environmental conditions. Next, we asked whether our data set could contribute to unravel the function of isoenzymes in the metabolic set. Previously proposed roles for isoenzymes include redundancy to buffer against mutations, a means to gene dosage or facilitation of evolutionary innovation and functional diversification. To address the role of isoenzymes in central metabolism, we used hierarchical clustering to analyze the abundance patterns of the metabolic proteins and their relationship to different functional classes and metabolic pathways. Interestingly, while subunits of the same protein complex preferably cluster in proximate branches, members of the same isoenzyme family often clustered in distant branches (Figure 5). The data therefore suggested functional diversification within most isoenzyme families and allowed to propose different functions of divergent isoenzymes. We expect that the comprehensive data set presented in this study will be an ideal blueprint for further developing models of yeast metabolism and for follow-up studies on the function of target metabolic proteins., Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.

Substrate metabolite concentrations are inversely related to the in vivo capacity of their converting enzymes. Local metabolite responses represent a passive mechanism to achieve metabolic homeostasis upon perturbations in enzyme capacity. Enzyme capacity and metabolite concentration control the metabolic reaction rate., Physiological behavior emerges from complex dynamic interactions between transcripts, enzymes, and metabolites, the constituents of metabolism, and its regulatory network (Sauer, 2006). Although large data sets can be generated on all these variables, data integration, in particular across different omics levels, is becoming the key challenge (Stitt and Fernie, 2003; Sauer et al, 2007). In this study, we identify a general relationship between substrates of an enzymatic reaction and enzymatic capacity in central carbon metabolism that allows the prediction of changes in metabolite concentration based on changes in enzyme capacity and vise versa. To elucidate whether general relationships exist between metabolite concentrations and enzyme capacities (i.e. the outcome of enzyme abundance combined with activity), we propose three hypothetical and alternative governing principles. The first hypothesis postulates a minimization of metabolite concentration at a given flux. In this case, no correlation between alterations in metabolite concentrations and enzyme capacities is expected. The second hypothesis postulates a tradeoff between metabolite concentration and enzyme capacity. In this case, a negative correlation between differences in concentrations of substrate metabolites and differences in enzyme capacity is expected. The third hypothesis postulates a minimization of enzyme capacity at a given flux. In this case, we expect a positive correlation between differences in concentrations of product metabolites and differences in enzyme capacity. As hypotheses I–III imply different relationships between enzyme capacities and metabolite concentrations, identification of the prevailing situation in microbial metabolism requires quantitative in vivo metabolite concentration and enzyme capacity data upon moderate changes in enzyme capacity. As a first test, we chose wild type Saccharomyces cerevisiae and an otherwise isogenic mutant with a complete deletion of the transcription factor Gcr2p, an activator of glycolysis (Chambers et al, 1995). This mutant exhibits altered transcript abundances, enzyme activities, and metabolite concentrations within closely connected reactions in glycolysis and in the tricarboxylic acid cycle (Uemura and Fraenkel, 1990, 1999; Sasaki and Uemura, 2005). To quantify the relationship between metabolite concentrations and enzyme capacities, we determined transcript, enzyme, and metabolite abundances in wild type and GCR2 mutant in batch culture on glucose minimal medium. Transcript and enzyme abundances are used as surrogates for enzyme capacities. The most significant correlation was observed for fold-changes in substrate metabolite concentrations with fold-changes in enzyme abundance. Not unexpectedly, enzyme abundances were a significantly better approximation for enzyme capacities than transcript abundances. A further improved correlation was achieved by considering all diverging enzymes that react upon a given substrate metabolite simultaneously rather than considering them as a separate reaction (Figure 4). The high correlation between substrate metabolite and enzyme fold-changes suggests a tradeoff between enzyme capacity and metabolite concentrations in central metabolism. To test the general validity for central carbon metabolism of the above-identified tradeoff between reaction substrate metabolite concentrations and enzyme abundances, we performed four independent validations: a statistical, a literature based, and two experimental ones. Statistically, we verified that the correlation between substrate metabolites and enzymes could not have been found by chance. On the basis of the literature data, we performed the above correlation analysis with literature data. All available data followed the proposed correlation, thus providing further evidence for the general validity of this relationship. As a more serious challenge of the identified correlation, we designed an experiment where the absolute flux alterations are large and additionally the flux directions are altered. We expected the substrate metabolites to occur at higher concentrations in the mutant than in the wild type. This expectation was fulfilled by the experimental data in all cases, thereby further corroborating the negative correlation between enzyme capacity and metabolite concentrations. So far, our experimental evidence was based on perturbing multiple enzyme abundances through a transcription factor mutant. To ensure that our findings are also valid for single-reaction perturbations, we modulated individual abundances of the four glycolytic enzymes Pgi1p, Tpi1p, Eno2p, and Cdc19p using strains whose endogenous genomic promotor was replaced by a Tet-controlled promotor (Mnaimneh et al, 2004) (Figure 7). Thus, we determined intracellular metabolites concentrations during exponential growth in the strains with modulated enzyme abundance. Our above-identified correlation predicts metabolite concentrations to increase only for the substrate of the such perturbed reaction and all other metabolite concentrations to remain constant. This prediction was verified. We demonstrate here that global or local alterations in enzyme abundance correlate negatively with enzyme reaction substrate concentration at least in central carbon metabolism. This implies a tradeoff between enzyme and metabolite efficiency in metabolic networks. These findings can be interpreted as a passive network mechanism to maintain close-to-wild-type homeostasis of central carbon metabolism upon perturbations that alter the enzyme capacity. The alterations are compensated by converse changes in reaction substrate concentrations, thereby minimizing the difference in metabolic flux that is caused by the alteration., What is the relationship between enzymes and metabolites, the two major constituents of metabolic networks? We propose three alternative relationships between enzyme capacity and metabolite concentration alterations based on a Michaelis–Menten kinetic; that is enzyme capacities, metabolite concentrations, or both could limit the metabolic reaction rates. These relationships imply different correlations between changes in enzyme capacity and metabolite concentration, which we tested by quantifying metabolite, transcript, and enzyme abundances upon local (single-enzyme modulation) and global (GCR2 transcription factor mutant) perturbations in Saccharomyces cerevisiae. Our results reveal an inverse relationship between fold-changes in substrate metabolites and their catalyzing enzymes. These data provide evidence for the hypothesis that reaction rates are jointly limited by enzyme capacity and metabolite concentration. Hence, alteration in one network constituent can be efficiently buffered by converse alterations in the other constituent, implying a passive mechanism to maintain metabolic homeostasis upon perturbations in enzyme capacity.