If a cell makes too much of a given protein, it can sometimes cause problems and impair the cell’s growth. Overproducing some proteins may deplete the cell’s limited resources, meaning it does not have enough to make other more essential proteins. This phenomenon is known as the protein burden effect. Theoretically, only harmless proteins can be overproduced up to a level where growth would be impaired in this way. Conversely, if an overproduced protein causes harm before it becomes a burden on resources, scientists must consider other mechanisms to explain the cell’s problems, namely that the protein itself is harmful.

Knowing the ultimate level of protein production that could cause the protein burden effect – the protein burden limit – would allow scientists to distinguish between harmful and non-harmful proteins. However, to date, this limit had not been defined for any cell.

Eguchi et al. have now tried to estimate the protein burden limit for budding yeast – one of the best-studied experimental organisms. The experiments first focused on enzymes involved in alcoholic fermentation because they were expected to be non-harmful. Some of these enzymes were overproduced to the level were the made up 15% of all the cell’s proteins before they started to cause growth defects. The same results were seen with versions of the enzymes that had been mutated to be less active, leading Eguchi et al. to conclude that this level is the protein burden limit.

In other experiments, harmful enzymes could only be overproduced to levels that were far less than this proposed protein burden limit. These enzymes caused problems for the yeast in several ways, including interfering with biochemical reactions and forming large aggregates in the cell. Lastly, Eguchi et al. looked at the yeast’s genetic code and saw that most of its genes seemed to have evolved to specifically limit the production of proteins to a level that would avoid the unwanted protein burden effect.

Together these findings establish a framework to clearly distinguish between harmful and non-harmful proteins. This framework will be useful to understand the different reasons why the overproduction of certain proteins, which is seen in neurodegenerative diseases and cancer cells, can cause problems for cells.

Protein overexpression is sometimes harmful to cellular growth (Makanae et al., 2013; Sopko et al., 2006), and a few mechanisms that could result in overexpression-triggered growth defects have been proposed (Moriya, 2015). Resource overload, stoichiometric imbalance, promiscuous interaction, and pathway modulation are triggered upon overexpression of, respectively, (i) a protein that has a high demand of cellular resources (Dong et al., 1995; Kintaka et al., 2016; Stoebel et al., 2008), (ii) a protein that is part of a protein complex (Kaizu et al., 2010; Makanae et al., 2013; Papp et al., 2003), (iii) a protein with a nonspecific interaction domain (Ma et al., 2010; Vavouri et al., 2009), and (iv) a protein that catalyzes a pathway (Prelich, 2012; Youn et al., 2017). The mechanism of protein overexpression-triggered growth defects depends on the protein’s structural and functional characteristics, which are not always fully understood yet. Therefore, it is still difficult to predict whether the overexpression of a particular protein will be harmful to cellular growth and which mechanisms cause the harmful effect.

The ultimate overexpression of a protein could be harmful for cellular growth, because it monopolizes and depletes limited resources that are involved in protein production, such as ribosomes and aminoacyl-tRNAs (Gong et al., 2006; Shachrai et al., 2010; Vind et al., 1993). This phenomenon is known as the protein burden/cost effect (Kafri et al., 2016; Snoep et al., 1995). Proteins that have no harmful effects on cellular functions can be overexpressed up to a level that causes protein-burden–triggered growth defects. Conversely, if a protein cannot be overexpressed up to that level because it adversely affects cellular functioning, then overexpression of that protein will cause growth defects at relatively low expression levels, and we should consider mechanisms causing the defects.

We previously developed a genetic tug-of-war (gTOW) method that can be used to estimate the expression limit of a target protein that triggers growth defects in the yeast Saccharomyces cerevisiae (Makanae et al., 2013; Moriya et al., 2006 , 2012 ). We estimated that the expression limit of a green fluorescent protein (GFP) was about 15% of the total cellular protein in S. cerevisiae (Kintaka et al., 2016). Because GFP is a highly structured cytoplasmic protein unrelated to the cellular functions of yeast and thus harmless, this level could be considered the expression limit for any protein that causes growth defects triggered by the protein-burden effect.

We predicted that the expression limits of some native highly expressed glycolytic proteins would be similar (>15% of the total cellular protein) (Moriya, 2015), suggesting that overexpression of these proteins would be harmless even though they have metabolic functions in yeast. The prediction was performed by the calculation of the proteins’ native expression levels (Kulak et al., 2014) and their gene copy number limits as determined by gTOW analysis, and has not yet been experimentally validated (Moriya, 2015). In this study, therefore, we tried to measure the expression limits of 29 glycolytic proteins to assess whether they are expressed up to levels that cause growth defects triggered by the protein-burden effect. There are five reasons why we chose glycolytic proteins: (1) because they are generally highly expressed and thus considered non-harmful upon high-level expression, they are excellent targets for examining whether they are expressed up to the protein-burden limit; (2) because they have been intensely studied, we have information that can allow us to manipulate their catalytic activities; (3) because the glycolytic pathway is one of the best-known metabolic pathways, we can predict and measure metabolic changes upon overexpression of these proteins; (4) they include a heteromer (Pfks), a mitochondrially localized protein (Adh3), and membrane proteins (Hxts), so we can assess how these characteristics affect expression limits; and (5) they include paralogs whose expressions are differently regulated, so that we can test how their differences affect their expression limits.

We found that the expression limits of most of the 29 proteins were comparable to that of GFP and were independently determined by their catalytic activities, as suggested by a kinetic model of yeast glycolysis, confirming that their overexpression was harmless. Also, some of the proteins had far lower expression limits than those that would create a protein burden (the expression limit of GFP), and their harmful effects were derived from their localization and metabolic perturbations. Owing to their codon optimality, native poorly expressed isozymes were not produced at levels sufficient to cause growth defects, even when they were expressed from the strong TDH3 promoter on the multicopy plasmids. Some glycolytic proteins formed S–S-bond-mediated aggregates when overexpressed, and this aggregation also seemed to restrict their expression limits.

According to the protein-burden concept (Dong et al., 1995; Kafri et al., 2016; Shah et al., 2013; Snoep et al., 1995; Stoebel et al., 2008), the ultimate overexpression of any protein could cause growth defects by overloading basic protein production resources. But only non-harmful proteins can be overexpressed up to the ultimate level, or the protein-burden limit, because the expression limit of harmful proteins should be restricted by their harmful effects. Knowing the protein-burden limit itself is thus essential when seeking to determine whether the overexpression of a protein is harmful to cellular functions. We previously estimated the protein-burden limit of S. cerevisiae cells by measuring the expression level of GFP that causes growth defects. This was 15% of the total cellular protein (Kintaka et al., 2016).

In this study, we first tried to measure the expression limits of yeast glycolytic proteins in order to confirm whether the protein-burden limit measured using GFP applies to endogenous proteins. Most of the glycolytic proteins studied here caused growth defects when they were expressed from a strong promoter on a multicopy plasmid (Figure 1). The expression levels of some glycolytic proteins in these conditions were, indeed, comparative or even higher than that of GFP (Figure 2). Also, their expression levels did not increase due to mutations in their catalytic centers (Figure 3A). These results strongly suggest that the protein-burden effect largely determines the expression limit, and that the limit is around 15% of the total cellular protein. Among the glycolytic proteins studied here, Pgk1, Gpm1, and Eno2 had the highest expression limits. Although Pgk1 (44.7 kDa) and Eno2 (46.9 kDa) are 1.5-fold larger than Gpm1 (27.6 kDa), their expression limits were similar to those of Gpm1 (Figure 2B). These results suggest that a protein's size does not affect its expression limit, at least for proteins in this molecular weight range. These data also suggest that the expression limits of proteins are not determined by the molar concentrations of those proteins but by the cost of the protein production.

Some other glycolytic proteins, such as Pfk1, Pfk2, Adh3, and Hxts, showed expression limits far below the protein burden limit of 15% (Figure 2), suggesting that overexpression of these proteins is harmful. Of the 18 glycolytic proteins studied, Pfk1 and Pfk2 were the only ones whose expression limits were significantly increased by mutations in their catalytic centers (Figure 3A–B), suggesting that their metabolic functions restrict their expression limits. We think, however, that the metabolic perturbations that trigged the overexpression only partially affect the expression limits because the expression limits of the mutant proteins were still far below those of other glycolytic proteins (Figure 3A–B). Pfk1 and Pfk2 form a hetero-octameric complex, and their stoichiometric imbalance leads to the formation of filamentous Pfk1 structures in the cytosol (Schwock et al., 2004). This stoichiometry-imbalance-triggered protein aggregate might cause growth defects upon overexpression of Pfk1 (and Pfk2), although we could not confirm this hypothesis because simultaneous overexpression of Pfk1 and Pfk2 did not increase the expression limits of these proteins (our unpublished observation).

The CC mutants of Fba1 and Pgk1 showed lower expression limits than their wild-type proteins (Figure 3A). We currently do not have any substantial and consistent explanation of why these CC mutants have lower expression limits. We can assume some general mechanisms: CC mutant proteins sequester the wild-type enzymes into inactive complexes; CC mutant proteins sequester the substrate molecules for the wild-type enzymes; or mutation in the catalytic center destabilizes the structure of the enzyme. For example, Fba1 is an essential homodimeric enzyme (UniProtKB: P14540). Overexpression of CC mutant Fba1 molecules might sequester active wild-type Fba1 molecules into inactive complexes. The limit of CC mutant Tdh3 was higher than that of the wild-type in low-copy conditions whereas it was lower in high-copy conditions (Figure 3A–B). This strange behavior might be related to its moonlighting function. The catalytic activity of Tdh3 did not seem to explain the difference in the expression limits of wild-type and CC mutant Tdh3 (Figure 5C). Beside its metabolic function, Tdh3 directly binds to Sir2 protein to promote transcriptional silencing, and a mutation in the catalytic center (C150G) reduces the silencing (Ringel et al., 2013). It is thus possible that the CC mutant Tdh3 (C150S) causes silencing in a dose-dependent manner by competing with wild-type Tdh3 for binding with Sir2.

We speculated that the localization of Adh3 to the mitochondria and of Hxts to the plasma membrane restricted their expression limits because localized proteins overload more-limited localization resources (Kintaka et al., 2016). This hypothesis was confirmed because the removal of the mitochondrial signal from Adh3 increased its expression limit (Figure 3C,D). We also speculated that the expression limits of membrane proteins such as Hxts should be restricted by their localization, although there is no experimental evidence to support this hypothesis yet.

The fact that the expression limits of most glycolytic proteins were not affected by mutations in their catalytic centers (Figure 3A) suggests that their overexpression does not cause metabolic perturbations. This finding was theoretically confirmed by simulations using a kinetic model of glycolytic metabolism (Figure 4). The reason why their overexpression does not cause metabolic perturbations is probably that they are bidirectional enzymes: the metabolic flux should be determined only by the availability of substrates when the concentrations of these enzymes are more than a certain level. To support this idea, the overexpression of 14 bidirectional enzymes showed minor metabolic changes, whereas the overexpression of 6 unidirectional enzymes (including Hxks, Pfks, Cdc19, and Pdc1) showed strong metabolic changes in the simulation (Figure 4). The expression limits of Hxks in the cells, however, were close to the protein burden limit (Figure 2B) and were not affected by mutations in the catalytic center (Figure 3A). These results suggest an additional mechanism that is not implemented into the model that allows cells to avoid the effects of big metabolic changes upon overexpression of Hxks: a mechanism that prevents these metabolic perturbations from occurring, or a mechanism that prevents these metabolic perturbations from causing growth defects.

Through the metabolic analysis, we realized that we currently do not have any systematic way to identify metabolic changes that are directly triggered by the overexpression of an enzyme, because metabolism is interconnected and the overexpression of a protein could cause non-specific perturbations that ultimately affect metabolism. Moreover, we know very little about how much change in which metabolite triggers a growth defect. Comparison of the metabolic changes in cells overexpressing wild-type and CC-mutant enzymes could be one solution for this. In fact, we observed a three-fold difference between cells expressing wild-type and CC mutant Pfk2 (Figure 5A). Nevertheless, once again, we cannot conclude from our current knowledge that this difference causes the difference in the expression limits of these two forms of Pfk2. By using a mathematical model, we tried to predict the potential metabolic changes that would be triggered by overexpression of an enzyme without considering unknown effects other than the enzyme's metabolic activity. In the simulations, overexpression of Pfks and Hxks triggered divergent and almost catastrophic metabolic changes (~1000-fold increase in some metabolites, Figure 4B,D), suggesting that their overexpression would cause growth defects due to these strong metabolic perturbations. We thus expected to obtain similar metabolic changes upon overexpression of Pfks, whose CC mutants had higher expression limits. We did not, however, observe such great changes (Figure 5A–B and Figure 5—figure supplement 1). To answer these issues precisely, we need a much deeper understanding of the connections between metabolite levels and cellular growth.

The translational rate of some glycolytic proteins, including Glk1, seemed low because of their lower codon optimality (Figure 6). Actually, the codon optimality of Glk1 (tAIg = 0.38) is close to the average for all the yeast genes (tAIg = 0.37), and the codon optimality of other glycolytic proteins studied here is exceptionally high (Figure 6—figure supplement 1). These observations suggest that the codon optimality of most yeast genes is not high enough to allow expression of their proteins up to the protein-burden limit, even if they are expressed from a strong promoter on a multicopy plasmid.

Overexpression of Eno2, Pgk1, and Tpi1 triggered S–S-bond-connected aggregation (Figures 7 and 8), and the aggregates that are formed contain other glycolytic proteins and translational factors (Figure 7C–D). We think that this aggregation is triggered by spontaneous non-specific S–S bond formation among proteins existing in high concentrations. Interestingly, we also detected the same proteins within the gel of the corresponding molecular weight in the vector control, although the amounts estimated by LC-MS/MS were lower and cannot be identified as visible protein bands (Figure 7—source data 1). Therefore, we speculated that the S–S-bond-mediated protein aggregation occurs even in normal physiological conditions, but it is accelerated by an increase in the concentration of cytoplasmic proteins upon overexpression of glycolytic proteins. This aggregation might affect the expression limits of cysteine-containing glycolytic proteins, because changing the cysteine residues of Tpi1 into serine residues increases the protein's expression limit (Figure 8B). As the amount of protein corresponding to the Tpi1 monomer was not changed by DTT treatment, the expression level of Tpi1 should not be reduced simply by aggregation but by the harmful effect of spontaneous S–S bond formation. This hypothesis is supported by the fact that the most highly expressed glycolytic protein Gpm1, which has a molecular weight similar to that of Tpi1, does not have a cysteine residue. The deleterious effect of this aggregation, however, seems protein-specific because the expression limits of Pgk1 and Eno1 were among highest measured (Figure 2A), and removal of their cysteine did not increase their expression limits (Figure 7—figure supplement 1).

As described above, we revealed mechanisms that restrict the expression limits of some glycolytic proteins. We do not think, however, that these mechanisms are the sole factors restricting the expression limits of these proteins. The expression limits of ΔMTS-Adh3 (0.45 AU, Figure 3D) and CoGlkl (1.07 AU, Figure 6A) are still lower than those of other high limit proteins such as Pgk1 and Gpm1 (2.26 AU and 2.63 AU, respectively, Figure 2B). It is thus likely that multiple mechanisms restrict the expression limits of these proteins.

Protein misfolding or misinteraction is considered to cause toxicity upon high-level expression of a protein with low translational robustness, low folding stability, or a high propensity for misinteraction (Drummond and Wilke, 2009; Zhang and Yang, 2015). In general, highly expressed proteins such as glycolytic proteins are thus evolved to avoid these characteristics (Zhang and Yang, 2015), and that should be a requirement for a protein to be expressed up to the protein-burden limit. Cdc19, one of the glycolytic proteins studied here, aggregates in a stress-induced and reversible manner through a region of low compositional complexity (Saad et al., 2017). This aggregation capacity of Cdc19 might explain why its expression limit (0.42 AU) is lower than the protein burden limit (>2.0 AU) (Figure 2B). Our finding in Figure 8 suggested that the high-level expression of a cysteine-containing protein could also cause a misinteraction-triggered toxic effect; hence unimportant cysteines should be avoided in highly expressed proteins. Concentration-dependent liquid phase separation is also considered to cause toxicity upon overexpression of structurally disordered and nucleic-acid-binding proteins (Bolognesi et al., 2016). We do not think that this mechanism caused growth defects upon overexpression of the glycolytic proteins studied here because they are less structurally disordered (Moriya, 2015) and not nucleic-acid-binding proteins.

We summarize our analysis in Supplementary file 1. In conclusion, we established the ultimate expression level that causes cellular growth defects due to the protein-burden effect as around 15% of the total cellular protein. The next interesting theme is to identify characteristics of proteins that can be overexpressed up to the protein-burden limit because such proteins are considered non-harmful to cellular functions. Those characteristics should conversely imply the properties of proteins that are harmful when they are overexpressed.

All data generated or analysed during this study are included in the manuscript and supporting files.

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Author details

1. Yuichi Eguchi

   Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan

   Contribution

   Data curation, Investigation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4809-1402

2. Koji Makanae

   Research Core for Interdisciplinary Sciences, Okayama University, Okayama, Japan

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

3. Tomohisa Hasunuma

   Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan

   Contribution

   Data curation, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Yuko Ishibashi

   Department of Life Sciences, School of Agriculture, Meiji University, Kawasaki, Japan

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

5. Keiji Kito

   Department of Life Sciences, School of Agriculture, Meiji University, Kawasaki, Japan

   Contribution

   Data curation, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

6. Hisao Moriya

   1. Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan
   2. Research Core for Interdisciplinary Sciences, Okayama University, Okayama, Japan

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Investigation, Writing—original draft, Writing—review and editing

   For correspondence

   hisaom@cc.okayama-u.ac.jp

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7638-3640

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