Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses.
Unicellular organisms are sensitive to environmental challenges. Their internal milieu acts as a buffer against such changes by mounting an adaptive response involving modifications at different cellular levels. Appropriate adaptive responses require intracellular signaling, changes in the conformation and activity of proteins, changes in transcription and translation of genes, etc. [1]. Many of the cellular modifications that characterize any adaptive response are due to the need for acquiring new protein functionalities while shutting down other protein functionalities that are not required in the new conditions. These changes ultimately fine tune the mechanisms and processes that allow the cell to function appropriately and survive under changing environments. Such fine tuning is shaped by various functional requirements and physiological constraints. The functional requirements are a result of the specific demands that are imposed on cell survival by the environment. On the other hand, the physiological constraints are defined by the limits within which the cell is physically capable of changing the activity of its component parts to meet the functional requirements. From a global point of view, adaptive responses can be seen as a multi-optimization problem because cells evolved appropriate responses to cope with different types of stress, while optimizing different parts of its metabolism for each of those responses [2], [3]. For example, cells simultaneously have to increase the concentration of specific metabolites and proteins, while decreasing the concentration of other components to prevent an increase in the concentration of unneeded metabolites. Such an increase could strain cell solubility capacity or increase spurious reactivity to dangerous levels. These and other functional constraints are likely to provide sustained evolutionary pressures that ultimately select a specific gene expression profile that leads to suitable adaptive responses. With these arguments in mind, it is thus important to identify the functional requirements and quantitative physiological constraints that may significantly shape adaptive responses. Among others, minimization of energetic expenditure plays an important role in cells growing exponentially in a rich medium. Several signatures that are consistent with minimization of metabolic cost have already been identified in the properties of the set of proteins that is expressed when cells are growing in rich media (basal conditions). For example, genes coding for proteins that are highly abundant under basal conditions have a pattern of synonymous codon usage that is well adapted to the relative abundance of synonymous tRNAs in the yeast S. cerevisiae and in Escherichia coli [4], [5]. Another signature that is found in genes that are highly expressed under basal conditions is a sequence bias that minimizes transcriptional and translational costs [6]. This minimization of metabolic cost is further observed in the relative amino acid composition of abundant proteins under the same conditions. These proteins are enriched with metabolically cheaper amino acids [7]. A final example of a general signature is the codon bias of long genes. This bias is such that the probability of missense errors is reduced during translation [6], [8], [9]. These biases suggest that reducing overall costs in metabolism, whenever possible, may significantly increase cellular fitness. This view is consistent with the observation that small changes in gene expression affecting the levels of protein synthesis influence the fitness of specific E. coli strains [10]. This body of results strongly supports the notion that metabolic cost acts as a selective pressure in shaping the properties of cells growing in a rich medium, in absence of environmental stresses. Thus, one might ask if minimization of metabolic cost is also an important factor in the evolution of adaptive responses to stress conditions. It is predictable that this evolutionary pressure might leave stronger signatures in adaptive responses that require the use of higher ATP amounts by the cell, such as adaptation to heat, weak organic acids, or NaCl. In these three cases, it has been reported that ATP concentrations decrease due to a high energy demand [11]. Given that protein synthesis is one of the costliest biosynthetic efforts for the cell [12], the minimization of metabolic cost might have biased the properties of proteins whose expression change during adaptation. Therefore, here we ask the following questions. Is there a signature that is consistent with a selective pressure for minimizing metabolic cost in proteins synthesis during adaptive responses to stress? Can one find general signatures in the physicochemical properties proteins and in the expression patterns of genes that are involved in the adaptive response to different environmental challenges? If so, what physiological constraints are consistent with those signatures? We address these questions by investigating how is the value of several properties of proteins (size and molecular weight of proteins, codon adaptation index, aromaticity, average cost per amino acid, etc.) related to changes in gene expression levels during various environmental changes. We find that genes whose expression is upregulated during different types of adaptive responses tend to code for proteins that are small, while genes whose expression is downregulated during the same responses tend to code for proteins that are large. This is a signature that is consistent with a selective pressure for minimizing metabolic cost in proteins synthesis. It is more significant in adaptive responses where changes in gene expression levels affect a large fraction of the genome. To our knowledge, this is the first general and global signature that has been identified for the properties of proteins involved in adaptive responses to stress.
The microarray data we analyze provide information regarding relative up and downregulation (UpCF and DownCF, respectively) of gene expression with respect to a pre-stress control condition. To facilitate comparison between upregulated and downregulated genes, we use the inverse of the ratio for downregulated genes. Thus, all values for the ratios of changes in gene expression discussed below are greater than 1. Changes in gene expression during stress responses are dynamic and, for the most part, transient. Because of this, we take the maximum value of up or downregulation as an approximated measure of the maximal change in gene expression during the transient stress response. Changes in gene expression are underestimated for genes that undergo very strong up or downregulation, due to intrinsic limitations of the microarray technology [26]. To minimize any errors that may come from this limitation we use the 98th quantile of all the ratio values for a given gene as a proxy of its maximum UpCF or DownCF.
Some of the protein properties we consider are strongly correlated (see Table S1). For example, different measures of codon preference towards the major tRNA isoacceptors, such as CAI, CB, and FOP, are highly correlated (r = 0. 83–0. 97). Length and molecular weight of proteins are, in practice, equivalent. Protein and mRNA abundance show a correlation of r = 0. 56. Protein abundance is also positively correlated to CAI, to CB and to FOP (r = 0. 53–0. 54), and to mRNA abundance under basal growth conditions (r = 0. 60–0. 64). Similarly, average amino acid cost (ACPA) is highly correlated to aromaticity (r = 0. 84), because the most expensive amino acids are aromatic. Thus, if a protein has a high percentage of aromatic amino acids (which is proportional to aromaticity) it will have a larger average cost per amino acid than proteins with lower percentage of aromatic amino acids. The only type of data that is available for both, the entire genome and a comprehensive set of yeast adaptive responses, is gene expression data from microarray experiments [13], [27]. Thus, in order to search for trends between the adaptive responses and the protein properties, we analyze how the value of those properties is related to the changes in gene expression during the response. We analyze microarray data for fourteen stress responses and two control conditions (change in carbon source — C Source — and return to basal conditions after osmotic shock — ↓Sorbitol). Short proteins with a high relative composition of metabolically cheaper amino acids are highly abundant under basal conditions, which is consistent with the hypothesis that lowering protein cost is a driving force in shaping the protein complement of yeast in those conditions [7]. It is also well known that the process leading up to protein synthesis is one of the costliest components of cellular metabolism [12] and that during response to many environmental stress signals yeast shuts down gene expression and decreases the number of ribosomes [13], [14]. It has been proposed that gene expression profiles have signatures that are specific to the conditions under which they have evolved [2], [28]. If metabolic cost in general, and cost of protein synthesis in particular, is a significant factor in shaping adaptive profiles, then one might expect that the stronger the resource limitation is, the larger its signature will be. It would then be reasonable to expect that adaptive responses where a resource limitation exists may have similar qualitative bulk expenditure in protein synthesis. To find support for this hypothesis we must estimate that cost for the different stress responses. Changes in protein levels can be roughly estimated over the whole genome by the changes in the levels of gene expression [29]. Thus, an index that approximately estimates the changing costs of protein synthesis during a given adaptive response i can be defined as: (1) In this equation Ak is the basal abundance of protein k and Lk is the primary sequence length of that protein UpCFik and DownCFik represent the change-fold of up- or downregulation of the gene that codes for protein k. It is likely that specific functional requirements during any given stress response will lead to the synthesis of new proteins whose functionality is required for survival under the new conditions. By calculating a cost index for each of the twenty five Gene Ontology (GO) categories of cellular components defined in the SGD Slim Mapper Tool, we can analyze if the requirement for new functions is restricted to specific categories of the GO classification or not. Such a discriminating cost index can be defined as: (2) The index refers to stress condition and GO category. For each protein within the GO category, the up- (UpCFijk) or down-change fold (DownCFijk) is multiplied by its length. If in the GO category the expression of genes coding for small proteins is preferably upregulated and the expression of genes coding for large proteins is preferably downregulated, the index will be negative. This index provides a rough bulk estimate of how much a cell invests in synthesizing new proteins (the upregulation term) subtracting how much the cell saves by decreasing the synthesis of other proteins (the downregulation term). The index under basal conditions is 0 because the difference between up- and down-expression is null in that case. A cluster analysis of the twenty five dimensional vectors built for each adaptive response with the index calculated for each GO category is shown in Figure 3. Four clusters can be distinguished from this analysis. Responses to ↓Sorbitol, C source, Menadione and Acid cluster together with basal conditions (Basal Cluster) and apart from the other responses. Interestingly, this Basal Cluster includes stress responses in which the previous analytical methods find a low correlation between protein cost and changes in gene expression (Tables 1–2 and Figures 1–2 and S1). Because we could not find an accurate bootstrap statistical test to calculate significance for the clusters in Figure 3 we further tested similitude between the conditions using a discriminant analysis of the data used to build the clusters. Two dimensions explain 99. 9% of the variance in the data and separate all four groups found in the cluster analysis (Figure S4). The normalized values of each component of the vector for each type of adaptive response plotted in Figure 4 show the similarity between the different responses. For reference purposes, the basal condition is represented by a dashed circle in each of the panels of that figure. Lines below that circle indicate negative values for while any lines above the basal condition circle indicate positive values for. Conditions included in the Basal Cluster show low absolute values for this index in all GO categories. This is different for the other groups that, overall, have larger negative values for in categories “Cytoplasm”, “Nucleus”, and “Ribosome”. The four clusters are also consistent with the gradation observed in the moving-quantile plots for length and abundance (Figure 2 and S1). Altogether, the results presented in this section, suggest two broad types of adaptive responses. In one type, corresponding to responses in the Basal Cluster, the changes in gene expression are small. In this group of responses, we find no correlation between protein properties and gene expression. In the other type of stress, responses have evolved in a way that is consistent with a significant pressure to minimize the metabolic cost of the response. The previous results suggest that the stress conditions considered can be classified in two broad types with respect to metabolic economy. On one hand, we have the Basal Cluster in Figures 3 and 4. This cluster includes the adaptive responses to Menadione, Acid and the two controls, C Source and ↓Sorbitol. The results do not support a significant pressure by metabolic economy in shaping these responses. On the other hand, all other responses can be clustered into three subgroups. Nevertheless, they all appear to be shaped to some degree by metabolic economy. Therefore, we lump together change-folds for gene expression of all these later stress responses. By doing this we create a data set that has a stronger signal than that found in responses to the individual stresses when we relate properties and gene expression changes. The stronger signal in this combined data set also allows us to analyze patterns within each GO category for function, biological processes and cellular location of the proteins.
What type of general selective pressure might explain the correlations we find between changes in gene expression and protein abundance or length during stress response? One answer to this question is that minimizing the cost of protein synthesis is a significant pressure that shapes changes in gene expression during adaptive responses. Why would minimizing metabolic costs improve fitness of S. cerevisiae? As the cell optimizes the expenditure of resources for metabolic maintenance, it will have more resources available for survival and reproduction, thus out competing organisms. This seem logical, but it also raises another question, which is why would one expect this pressure to be felt at the level of proteins? Calculations based on the typical cellular composition of yeast and bacteria predict that protein synthesis uses more metabolic resources and ATP molecules than the formation of other macromolecules and it is a limiting step for yield [12], [34], [35], [36], [37]. As proteins of a shorter size use less amino acids, evolving fully functional short proteins leads to faster protein synthesis with less usage of cellular resources. It must be stressed that this argument cannot be seen as defending that cell will, over time, simply loose all large proteins and use smaller proteins to perform all necessary molecular functions. Evolution is constrained by life history. Specifically, the evolutionary unit of proteins is the functional domain [38]. Such functional domains have on average appeared only once in evolution and examples of domain convergent evolution are rare. Protein function often depends on how a small number of amino acids are located within the 3D structure of these domains. Therefore, a shorter protein may not have a 3D structure that will allow for the maintenance of an appropriate biological activity. This will constrain the amount of resources that can be saved by evolving shorter proteins. Under stress, availability of resources may be significantly limited, and the cell must adapt quickly in order to survive. For challenging stress conditions, resource limitation may impose severe limitations to the adaptive response. Exposure to these kinds of stresses causes the cell to deviate considerable resources from its steady state metabolism towards the adaptive response and imposes important constraints to cell economy [11], [39]. For example during exposure to high NaCl concentrations, additional energy expenditure for growth increased between 14% and 31% [40], and the activity of the plasma membrane H+-ATPase (highest consumer of ATP) is repressed during heat shock or in the presence of a weak acid [11], [41]. Another situation that has been put forward as supporting a cellular energetic shortage during stress response is the hypersensitivity to oxidative stress of mutants that lack mitochondrial function and of yeast treated with mitochondrial inhibitors [42]. These authors suggest that the oxidative sensitivity is due to a defect in an energy-requiring process that is needed for detoxification of ROS or for the repair of oxidative molecular damage. Further support for the importance of protein cost as a selective pressure in the evolution of adaptive changes in gene expression is found in different studies. For example, pathways appear to have evolved to maximize flux for a minimum amount of protein, because the enzyme concentration may be limited by both the protein synthesizing capacity and the solvent capacity of a cell [43]. In fact, theoretical studies suggest that adaptive responses of yeast to environmental changes trigger a gene expression profile that is optimal under the constraint of minimal total enzyme production [2], [44], [45]. There are three aspects that the cell can tune to decrease cost of protein synthesis. First, it can decrease the amount of protein that it synthesizes per time units. If we take changes in gene expression as a proxy of changes in protein synthesis, we find that, in many cases the overall protein synthesis during stress response is decreased (the yij index defined above is negative). Second, the cell may decrease cost of protein synthesis by expressing at higher levels proteins that are small. This would decrease the biosynthetic cost per protein chain and is consistent with our results. Finally, the cell may decrease the cost of protein synthesis by increasing the half life of proteins. We find no evidence for this strategy. In summary, if decreasing the cost of protein synthesis significantly contributes to shaping the gene expression profile of an adaptive response, we should find trends in the composition of the changing protein complement that are consistent with the following predictions: The results of our analysis are broadly consistent with these predictions (see Figure 3 for a summary) and support the hypothesis that response to the various stresses has evolved under a selective pressure for minimizing the cost of protein synthesis. GO analysis show that the results are not biased by a specific type of proteins and that the hypotheses are consistent with the results over a wide variety of GO categories. We also see that proteins involved in molecular complexes have changes in gene expression that are similar to proteins that are very large. A more detailed analysis of this later result would require an accurate knowledge about the stoichiometry of the complexes. Further analysis that would directly establish whether there are limitations on resources and energy usage during a given adaptive response would require data about ATP usage and production under each relevant condition. Such data would allow us to better understand which constraints are important in shaping the evolution of those responses.
Although different environmental stresses trigger specific sets of protective changes in the gene expression of yeast, the adaptive responses to these stresses also share some common features. We hypothesize that minimization of metabolic costs may contribute to shaping such adaptive responses. If this is so, then such pressure should be more noticeable in the costliest biosynthetic processes. One of these is protein synthesis. Thus, we analyze the set of genes and proteins whose expression changes during the responses and look for evidence to support or falsify our hypothesis. We find that protein properties that are indicative of protein cost correlate to changes in gene expression in a way that is consistent with that hypothesis for a large number of adaptive responses. However, if changes in gene expression are small during the adaptive response, we find no evidence of protein cost as a factor in shaping the adaptive response.
biochemistry/transcription and translation
microbiology/microbial evolution and genomics
computational biology/systems biology
evolutionary biology/bioinformatics
Minimization of Biosynthetic Costs in Adaptive Gene Expression Responses of Yeast to Environmental Changes