To evaluate the expression limits of FPs, we overexpressed FPs to levels that induced growth inhibition. Gene constructs, in which the TDH3 promoter controls the target protein genes, were incorporated into plasmids designed for the genetic-tug-of-war (gTOW) method (Figure 2A, Figure 2—figure supplement 1A; Moriya et al., 2006). Subsequently, yeast cells were transformed and cultured in a medium depleted of leucine. The maximum growth rate (MGR) of cells was calculated from serial measurements of optical density at 660 nm (OD₆₆₀). Protein levels were determined through the analysis of proteins from cells in the logarithmic growth phase (Figure 2—figure supplement 1B). For each protein, we also analyzed a mutant in which the Tyr residue forming the fluorophore was replaced with Gly (YG mutation), resulting in a loss of fluorescence. This is because we unexpectedly found that this mutation substantially increased the expression limits of FPs (see below).

Overexpression of the FPs and mutants all caused growth inhibition (Figure 2B and C). EGFP and mox were expressed at about 15% of the total protein as previously reported (Kintaka et al., 2016), but mCherry (mChe) and YG mutants were expressed at higher levels. Mox-YG expressed 44% of the total protein (Figure 2D and E). Our previous work had evaluated only expression limits (Eguchi et al., 2018), but we realized that we could not properly assess the constraints on each protein if the growth rates at the expression limit were different. For example, the mChe’s expression limit (20%) is higher than that of sfGFP (17%), but the relative MGRs are 20% and 80%, respectively, which is much lower for mChe. The relationship between %MGR and %protein level when the control is set at 100% is shown in the scatter plot in Figure 2F. The lower left indicates more constraint as lower expression levels lead to growth inhibition, and the upper right indicates less constraint as higher expression levels do not cause growth inhibition. Here, we describe ‘unconstrained’ as ‘neutral’ and calculate the neutrality index (NI) as the product of %MGR and %protein level.

Note that the regime of NI in Figure 2F shows a theoretical neutrality distribution but does not show the relationship between growth rate and the expression level when the expression level of a protein with a particular neutrality is increased or decreased. For example, 20% of the growth rate would be maintained when the expression of mox-YG (NI = 2400) is increased to 100%, but this is not realistic. We also note that in this study, we calculated NI using the percentage of the target protein among total cellular proteins separated by SDS-PAGE. Therefore, to calculate NI, the expression limit of the target protein must be sufficiently high to be detectable by this method. On the other hand, if detection sensitivity is improved, for example, by using western blotting, it becomes possible to estimate the NI even for proteins under strong constraints—that is, those that exhibit cytotoxicity. In fact, when we recalculated the NI for EGFPs fused with various localization signals from our previous study (Kintaka et al., 2016), all values were found to be below 100 (Figure 2—figure supplement 3).

The NI of each protein indicates that introducing a non-fluorescent mutation in FPs uniformly increases neutrality (Figure 2G), suggesting that ‘having fluorescence’ is a constraint on the expression of FPs. The most neutral proteins were sfGFP-YG and mox-YG, with NIs of about 2400. Notably, despite significant differences in the degree of growth inhibition and expression limits between the two proteins, their NIs are nearly identical (Figure 2F and G). They differ only in two amino acid residues (Cys - Ser; Figure 2—figure supplement 4), which seem to generate differences in expression efficiency. However, this does not appear to affect their NIs.

We also investigated the impact of expression efficiency on NI with another FP, mChe. The mChe used in this study is codon optimized (the codon adaptation index (CAI)=0.584; Keppler-Ross et al., 2008), while the mChe used by Kafri et al. for protein cost analysis (here referred to as mChe-Kafri) is not codon optimized (CAI = 0.236; Kafri et al., 2016). When overexpressed, mChe-Kafri (and mChe-Kafri-YG) had lower expression and growth inhibition than mChe (and mChe-YG; Figure 2—figure supplement 5A–D). This may be due to the lower translation rate of mChe-Kafri mRNA. However, their NIs were not significantly different, especially for the YG mutation (Figure 2—figure supplement 5E, F). Thus, the constraints indicated by NI are apparently not affected by translation efficiency. In the study using this mCherry-Kafri, it was also reported that the more mChe-Kafri was expressed, the more the cells enlarged (Kafri et al., 2016). However, no cell enlargement was observed with mox-YG, which can be expressed more than mCherry. Interestingly, the mChe-YG cells did not show enlargement (Figure 2—figure supplement 6A, B), indicating that cell enlargement is a phenomenon that occurs specifically when fluorescent mChe is overexpressed.

It should be noted that the growth inhibition caused by the overexpression of mox-YG in this study is a burden on translation rather than on transcription. This is because overexpression of a frameshift mutant (moxFS), in which four nucleotides were inserted directly after the ATG of the mox-YG gene, abolished the notable growth delay (Figure 2—figure supplement 6C, D).

Next, we further analyzed the possibility that fluorescence constrains the expression of FPs. When yeast was grown in the dark, the degree of growth inhibition of mox was the same (Figure 2—figure supplement 7A) and the expression limit of mox was rather increased (Figure 2—figure supplement 7B). Phototoxicity does indeed exist when expressing mox with fluorescence. This is because, when exposed to strong light, cells overexpressing mox show a reduced growth rate (Figure 2—figure supplement 7C–E) and abnormalities in their internal structures (Figure 2—figure supplement 7F). However, the expression limits of mox and mox-YG were almost unchanged by the light (Figure 2—figure supplement 7G, H). Thus, the constraint was not due to phototoxicity itself.

The number of Tyr residues was also irrelevant because NI was like that of mox-YG when a single Tyr was added to the C-terminus of mox-YG (Figure 2—figure supplement 8A–E). Reactive oxygen species (H₂O₂) are generated during fluorophore formation. When H₂O₂ was added to the medium, stronger growth inhibition occurred only in mox (Figure 2—figure supplement 8F–H). This suggests that the reactive oxygen species generated during fluorophore formation are responsible for the constraint. However, this possibility is not supported by the omics analysis described later (see Discussion). Mox-YG, like mox, was present throughout the cytoplasm and did not cause any characteristic localization or aggregation (Figure 2—figure supplement 9).

During this study, we examined a moxGFP (mox-T203I) in which Thr203 was accidentally replaced by Ile (Figure 2—figure supplement 10). The fluorescence of this mutant is reduced to 78% of mox (Figure 2—figure supplement 10A–C). The growth rate of the cells expressing this mox-T203I was like that of mox and mox-YG (Figure 2—figure supplement 10E). Still, the expression limit was intermediate between mox and mox-YG (Figure 2—figure supplement 10F), resulting in the NI intermediate between mox and mox-YG (Figure 2—figure supplement 10G). This result further supports that fluorescence is related to constraints. However, as mentioned above, fluorescence emission itself should not be toxic (Figure 2—figure supplement 7). We next focused on the formation process of the fluorophore: the tyrosine of the GFP fluorophore undergoes phenol and phenolate state changes, with Thr203 stabilizing the phenol form and substitution to Ile reducing the phenolate form (Kummer et al., 2000). Furthermore, in enhanced GFPs such as mox, substitution of Ser65 before Tyr66 for Thr anchors the phenolate form. To investigate the possibility that this is a factor determining the limit of mox, we produced mox with Thr65 replaced with Ser and examined its expression limit. The mox-T65S mutant was expressed 12% more than mox (Figure 2—figure supplement 10H, I), suggesting that the phenol-type fluorophore can be a limiting factor for the expression level of the fluorescent protein, while the concrete mechanism is still unclear.

To understand the cell physiology under protein burden, we next performed transcriptome (RNA-seq) analysis of the cells overexpressing mox-YG, the least constrained protein. Although the expression limit of mox-YG is about threefold higher than that of mox (Figure 2E), the growth rates at the expression limits of both are not largely different (Figure 2C). Thus, by comparing transcriptomes upon their overexpression, we may be able to distinguish and extract the general transcriptional response due to reduced growth rates from the (protein burden) response associated with the massive expression of a less constrained protein.

The expression changes under mox and mox-YG overexpression were calculated as fold change (FC) over the vector control (Figure 3—figure supplement 1A, B). The overall trend of expression changes was similar (r=0.64, Figure 3A). This similar variation may include responses associated with the expression system and reduced growth rate. On the other hand, there were also different transcriptional responses between the two. Gene categories that showed significant expression changes in either mox or mox-YG overexpression are shown in Figure 3B. In addition to the enhanced ribosome synthesis and decreased glycolysis observed in previous mox overexpression (Namba et al., 2022), enhanced oxidative phosphorylation was detected. Interestingly, ‘Ribosome biogenesis’, which was elevated in mox overexpression, showed a rather decreasing trend in mox-YG overexpression (Figure 3B). Indeed, IFH1, which is involved in ribosomal gene transcription (Martin et al., 2004), was upregulated in mox, but downregulated in mox-YG (Figure 3A). Different trends were also observed for ‘amino acid metabolism’ (Figure 3B). The amino acid transporter genes AGP1 and GAP1 were upregulated only in mox-YG overexpression (Figure 3A and C). Expressions of specific amino acid and nitrogen source transporter genes were strongly induced in mox-YG (Figure 3C). The expression of glucose transporter genes was also very different (Figure 3C). These results suggest that mox and mox-YG induce similar but distinct transcriptional responses, particularly that mox-YG overexpression may result in depletion of amino acid and nitrogen sources and decreased ribosome synthesis.

The transcriptional responses in mox-YG overexpression obtained by RNA-seq were confirmed by transcriptional reporter analysis (Figure 3D, Figure 3—figure supplement 2). In this reporter analysis, we also tested overexpression of a frameshift mutant (moxFS) that imposes a burden on transcription but not on translation (Figure 2—figure supplement 6C, D). As a result, most of the responses observed under mox-YG overexpression were not observed under moxFS overexpression, suggesting that these transcriptional responses are likely caused by a burden on translation. The only exception was TDH3pro, whose activity decreased even under moxFS overexpression. Since TDH3pro is used for driving overexpression in this system, this result suggests that transcriptional competition may occur as a result of overexpression. Notably, in our gTOW experiments, leucine depletion is employed to induce overexpression; however, we consider it unlikely that leucine depletion per se is responsible for the induction of GAP1, because GAP1 expression remained unchanged in the vector control even under gradual leucine depletion (Figure 3—figure supplement 3).

Protein burden is an overload on synthetic resources, and thus, when this overload occurs strongly, it is expected that there would be a reduction in the amount of proteins other than those being overexpressed due to competition for synthetic resources. Indeed, in cells overexpressing mox-YG, the amounts of proteins other than mox-YG were reduced (Figure 4A). Interestingly, the total amount of protein per cell remained almost constant, with the amount of other proteins decreasing as mox-YG was overexpressed. This trend was generally true for the overexpression of other FP (Figure 4—figure supplement 1). This suggests that there is a mechanism (or constraint) that maintains the overall cellular protein levels.

The maintenance of total cellular protein levels under overexpression conditions may be due to constraints such as intracellular space limitations or limitations in the availability of nutrients in the medium. To investigate whether nutrient limitation contributes to this phenomenon, we attempted overexpression of mox and mox-YG in YPD medium, which is considered to be more nutrient-rich than the SC medium. Since leucine depletion could not be used to drive overexpression in this context, we employed an alternative strategy using the aureobasidin A resistance marker (AUR1d) to increase plasmid copy number (Figure 4—figure supplement 2A). In this experiment, mox-YG was expressed at approximately 20% of the total cellular protein, with a corresponding increase in total protein content per cell (Figure 4—figure supplement 2C, D). This result suggests that in nutrient-rich YPD medium, cells may possess the capacity to buffer excess protein expression by increasing the total proteome. However, under these expression conditions, no growth inhibition was observed (Figure 4—figure supplement 2B), and thus the induction of mox-YG was not sufficiently strong. We therefore consider it necessary to investigate the regime under conditions of stronger overexpression in future studies.

To examine changes in protein expression, proteomic analysis of mox-YG overexpressing cells (in the SC medium) was performed. The distribution of intensities obtained from the analysis is shown in the Proteomaps in Figure 4B. In mox-YG overexpressing cells, mox-YG accounted for 40% of the total protein expression. Figure 4A suggests that in mox-YG overexpressing cells, proteins other than mox-YG are overall reduced to about 60% compared to the vector. Therefore, we considered it necessary to normalize the data overall to accurately represent changes in the per-cell proteome. However, in this study, we used data without such correction to focus on how the composition of the proteome, other than mox-YG, changed (see Materials and methods and Figure 4—figure supplement 3A–C for more details).

Figure 4C illustrates the categories that showed significant changes upon mox-YG overexpression (all categories are shown in Figure 4—figure supplement 4). Elevated mitochondrial proteins, such as oxidative phosphorylation and TCA cycle, and decreased ribosomal protein and ribosomal synthesis were observed. Interestingly, the proteomic changes observed in mox-YG overexpressing cells showed little correlation with the corresponding transcriptomic changes (Figure 4—figure supplement 3D). The only notable exception was oxidative phosphorylation, which was upregulated at both the transcript and protein levels (Figures 3B and 4C). In contrast, for example, ribosomal proteins were upregulated at the transcript level but markedly downregulated at the protein level (Figures 3B and 4C). This discrepancy may indicate a cellular state in which transcriptional responses are uncoupled from protein expression as a result of protein burden.

As mentioned above, RNA-seq results suggested nitrogen source starvation in mox-YG overexpression. Therefore, we investigated whether this response is influenced by the regulation through the TORC1 pathway, a primary mechanism responsive to nitrogen sources. By comparing expression changes due to rapamycin (a TORC1 inhibitor) treatment (Gowans et al., 2018) and mox-YG overexpression, we found that about 40% of the proteins with altered expression were congruent with the group of proteins regulated by TORC1 (Figure 4—figure supplement 5A, B). Thus, the proteome expression changes due to mox-YG overexpression may include responses due to inactivation of the TORC1 pathway as well as other responses. Figure 4C shows which categories had significant expression changes when separated into rapamycin-responsive (Rap) and rapamycin-non-responsive (Non-Rap) gene populations. Carbohydrate metabolism, elevated TCA cycle, and decreased ribosome synthesis were suggested to be responses due to TORC1 inactivation, while endocytosis, cytoskeleton, protein transport and lipid synthesis, and glycolysis were suggested to be due to responses other than TORC1 inactivation (Figure 4—figure supplement 6). Oxidative phosphorylation included both Rap responses (12/32) and non-responses (20/32) (Figure 4—figure supplement 6C). Indeed, mox-YG overexpression reduced the sensitivity to rapamycin compared to the vector control (Figure 4—figure supplement 7A–E), supporting the notion that TORC1 activity was diminished. However, we were unable to obtain direct evidence demonstrating TORC1 inactivation. We examined the phosphorylation status of Atg13 (Kamada et al., 2000), a known TORC1 target, by Western blotting. In mox-YG overexpressing cells, however, the total expression level of Atg13 was markedly reduced (Figure 4—figure supplement 7F). Therefore, it remains inconclusive how the decreased expression of TORC1 targets affects downstream signaling under these conditions.

Above transcriptome and proteome analyses suggested that mox-YG overexpression induces mitochondrial development and a metabolic shift from fermentation to oxidative respiration. To verify mitochondrial development, we performed fluorescence microscopy. Indeed, mitochondria labeled with Rip1-mScarlet-I, a component of the oxidative phosphorylation pathway, were significantly more developed in mox-YG overexpressing cells (Figure 4D, Figure 4—figure supplement 8). Furthermore, oxygen consumption was increased in these cells (Figure 4E, Figure 4—figure supplement 9), supporting the notion that they had undergone a metabolic shift toward oxidative respiration.

Ribosomes are a crucial factor for protein burden because they are themselves protein synthesizers while being synthesized in the highest amounts in the cell. Therefore, we further focused on the behavior of ribosomes in mox-YG cells, and expression changes were investigated in detail for each ribosomal protein (Figure 5—figure supplement 1). While the majority of the 84 ribosomal proteins detected (45 of them significantly) decreased, six (Rps12, Rps31, Ppp0, Rpp2a, Rpl5, and Rpl12) showed significant increases. Interestingly, all decreased ribosomal proteins are assembled into ribosomes in the nucleolus, while all increased ribosomal proteins are assembled into ribosomes in the cytoplasm (Lafontaine, 2015). They constitute the ‘beak’ of the 40 S subunit and the ‘P-stalk’ and the ‘central protuberance (pc)’ of the 60 S subunit (Figure 5A). Electron micrographs showed an abnormal nucleolar formation of mox-YG overexpressing cells (Figure 5B, Figure 5—figure supplement 3), in addition to reduced ribosome density in the cytoplasm (Figure 5—figure supplement 2A). This abnormal nucleolar formation was also confirmed by fluorescence microscopy images of Nsr1 (Nucleolin), the major protein of the nucleolus (Figure 5—figure supplement 2B–D).

Based on these results, we hypothesized that overexpression of mox-YG creates an imbalance between the amount of ribosomal protein synthesized in the cytoplasm and the amount of rRNA synthesized in the nucleolus (Figure 5C and D). The imbalance may lead to the creation of misassembled ribosomes, which are eventually degraded. We speculated that the primary enzyme degrading the misassembled ribosome should be the nuclear exosome. This is because our previous genome-wide mutation analysis identified exosome mutants (dis3-1, mtr3-ts, mtr4-1, rpn42-ph, rpn45-ph, and ski6-ph) as primary mitigators of protein burden (Kintaka et al., 2020). We confirmed that the mtr4-1 mutant does not show growth retardation upon mox-YG overexpression (Figure 5E). In fact, in mtr4-1 cells, there was no difference in growth rate between the vector control and mox-YG overexpression (Figure 5—figure supplement 5B), but rather a shorter lag time for the mox-YG overexpression than the vector control (Figure 5E). We next quantified protein upon mox-YG overexpression in mtr4-1 cells (Figure 5F). Compared to the wild-type (Vector), the total protein content in mtr4-1 (Vector) cells was reduced to 78%, which was likely related to the decreased growth. In the mtr4-1 mox-YG overexpressing cells, the amount of other proteins decreased (to 54%), overwhelmed by the 40% expression of mox-YG. However, the overall protein content increased compared to the mtr4-1 vector control, with a composition like the wild-type mox-YG overexpressing cells. Therefore, in mtr4-1 cells, even though mox-YG overexpression reduced the amount of other proteins (to 54%) compared to the vector control (78%), the growth rate was not reduced (Figure 5—figure supplement 5B). This suggests that mox-YG overexpression restores the effect of growth inhibition caused by mtr4-1. Next, we observed the composition of the nucleolus in mtr4-1 (Figure 5G, Figure 5—figure supplement 5D, E). The failure of nucleolus formation observed upon mox-YG overexpression was partially, but significantly, rescued in the mtr4-1 strain. This supports our hypothesis that misassembled ribosomes are degraded by the exosome (Figure 5D), and with low exosome activity, nucleolus formation (and efficient ribosome assembly) would be possible even with overexpression of mox-YG (Figure 5—figure supplement 4D).

While experimenting with mox-YG overexpressing cells, we found that these cells stop growth earlier (lower final density) at higher temperatures (38 °C) than the vector controls (Figure 5H, Figure 5—figure supplement 6A). Cells at this time did not show cell death such as cell bursting (Figure 5—figure supplement 6B). Recently, it was reported that intracellular osmotic pressure increases at high temperatures, resulting in less formation of liquid-liquid phase-separated structures such as nucleoli, which are recovered when cells are placed in hyperosmotic conditions (Watson et al., 2023). We hypothesized that the growth inhibition of mox-YG overexpressing cells at high temperatures may be due to the enhanced failure of nucleolus formation, and thus cultured mox-YG overexpressing cells under hyperosmotic pressure. At 30 °C, hyperosmolarity caused the same decrease in growth for the vector control and mox-YG overexpression (Figure 5I, Figure 5—figure supplement 6C). On the other hand, at 38 °C, hyperosmolarity caused a decrease in growth only in the vector control and no further decrease in mox-YG overexpression cells (Figure 5J, Figure 5—figure supplement 6D). The same trend was observed regarding the size of the nucleolus (Figure 5—figure supplement 6E, F). Under the conditions with added sorbitol and under mox-YG overexpression, the size of the nucleoli was smaller than the control at 30 °C, whereas, at 38 °C, it was rather larger than the control. This suggests that mox-YG overexpression and hyperosmolarity may mitigate each other’s growth-inhibitory effects, and one explanation may be that hyperosmolarity suppresses the abnormal organization of the nucleolus caused by mox-YG overexpression.

Thus far, our investigation has focused on heterologous fluorescent proteins. Finally, we turned to endogenous yeast proteins to identify those with the lowest cytotoxicity and to examine the phenotypic consequences of their overexpression. We previously investigated the expression limits of a group of glycolytic enzymes in S. cerevisiae, with Gpm1 and its catalytic center mutant (CCmut) having the highest expression limits (Eguchi et al., 2018). Here, we evaluated the neutrality of the wild types and their CC-mutants of Tdh3 and Gpm1 (Figure 6A, Figure 6—figure supplement 1). The growth rates at the expression limits were higher in the CCmut for both Tdh3 and Gpm1, and in Gpm1, the CCmut grew twofold faster (Figure 6—figure supplement 1A, C). The protein levels of wild types and mutants of both proteins were not significantly different (Figure 6—figure supplement 1D, E). The calculated NIs were highest for Gpm1–CCmut, which was equivalent to that of mox-YG (Figure 6A). We previously concluded that the activities of glycolytic enzymes generally do not affect their expression limits (Eguchi et al., 2018), but their neutralities suggest that their activities can become constraints in some way. This may be due to a disturbance in metabolism or a property of the active enzyme, such as an effect of reactive amino acids (His or Cys, Figure 6—figure supplement 2).

Although Gpm1 and Gpm1–CCmut exhibited NIs comparable to that of mox-YG, suggesting that they are similarly unconstrained, notable differences in cell morphology were observed (Figure 6B, Figure 6—figure supplement 3). Cells overexpressing Gpm1 appeared enlarged and spherical, while those overexpressing Gpm1–CCmut did not show such hypertrophy but were still rounder than vector controls or mox-YG overexpressing cells. The transcriptional responses observed in mox-YG overexpressing cells, including the induction of GAP1, a marker of the amino acid starvation response, were not detected in Gpm1–CCmut overexpression (Figure 6C, Figure 6—figure supplement 4). In Gpm1–CCmut overexpressing cells, similar to mox-YG overexpression, the levels of proteins other than Gpm1–CCmut were reduced, despite a slight, statistically non-significant increase in total protein amount (Figure 6—figure supplement 1F). While mitochondrial development was observed in Gpm1–CCmut cells using Rip1–mScarlet-I labeling (Figure 6—figure supplement 5), no increase in oxygen consumption was detected (Figure 6D, Figure 6—figure supplement 6). The nucleolus, visualized with Nsr1–mScarlet-I, appeared similarly shrunken in Gpm1–CCmut overexpressing cells as in mox-YG overexpressing cells (Figure 6E, Figure 6—figure supplement 7). These results suggest that the phenotypes observed in mox-YG overexpressing cells include both mox-YG-specific effects and responses commonly associated with protein burden. Alternatively, as indicated by the abnormal cell morphology, Gpm1–CCmut may possess unknown constraints or cytotoxic properties not captured by the neutrality index, which could obscure the detection of phenotypes typically associated with protein burden.

BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) (Brachmann et al., 1998) was used as the host strain for the experiments. Yeast culture and transformation were performed as previously described (Amberg et al., 2005). A synthetic complete (SC) medium without uracil (Ura) or leucine (Leu), as indicated, was used for yeast culture. Other strains used in this study are listed in the Key Resource Table.

The plasmids were constructed by homologous recombination activity of yeast cells (Oldenburg et al., 1997), and their sequences were verified by DNA sequencing. Plasmids used in this study are listed in the Key Resource Table.

To overexpress a target protein to a level that causes growth inhibition (in this study, this is referred to as ‘expression limit’), we used gTOW (Figure 2—figure supplement 1A; Moriya et al., 2012; Moriya et al., 2006). The gene of the target protein is incorporated into the gTOW plasmid (here, pTOW40836) and introduced into yeast cells lacking ura3 and leu2 genes. The selection marker in this case is uracil (–Ura conditions). Since this plasmid has the replication origin of 2 µm plasmid (2 µ ORI), this plasmid becomes multicopy in the cell (about 30 in the case of the vector). When yeast cells carrying the plasmid are transferred to a medium without leucine and uracil (–Leu/Ura conditions), the copy number of the plasmid in the cells increases (to about 120 in the case of the vector). This is because leu2-89 on the plasmid is a LEU2 allele with a large deletion in its promoter, so cells with higher plasmid copy numbers will grow faster in –Leu/Ura conditions. In other words, leu2-89 acts as a bias to raise the plasmid copy number in –Leu/Ura conditions. If overexpression of the target protein inhibits growth, the plasmid copy number will be lower than the copy number that gives rise to the critical expression of that protein. In other words, the target gene acts as a bias to lower the plasmid copy number. The resulting tug-of-war between the selection biases created by the two genes leads to a target protein being expressed at close to the critical expression level in –Leu/Ura conditions (the green-colored area in Figure 2—figure supplement 1A). By using a version of the aureobasidin A resistance gene (AUR1) with a deletion in its promoter (AUR1d) as a bias to increase plasmid copy number, experiments based on the same concept as gTOW can also be conducted in rich medium (YPD + 500 ng/mL aureobasidin A). In this case, the plasmid pTOW-AUR1d is used (Figure 4—figure supplement 2A).

Cellular growth was measured by monitoring OD₆₆₀ every 10 min using a compact rocking incubator (TVS062CA, ADVANTEC). The max growth rate (MGR) was calculated as described previously (Moriya et al., 2006). Average values, SD, and p-values of Welch’s t-test were calculated from at least three biological replicates. The growth data of Figure 2—figure supplement 10A was measured by monitoring OD₅₉₅, respectively, every 10 min using a microplate reader (Infinite F200, TECAN).

Yeast cells overexpressing a target protein were cultivated in SC–Leu/Ura medium. The total protein was extracted from log-phase cells (OD₆₆₀ = 1.0) with a NuPAGE LDS sample buffer (Thermo Fisher Scientific) after 0.2 mol/l NaOH treatment (Kushnirov, 2000). For each analysis, the total protein extracted from 1 optical density (OD) unit of cells with OD₆₆₀ was used. For total protein visualization, the extracted total protein was labeled with Ezlabel FluoroNeo (ATTO), as described in the manufacturer’s protocol, and separated by 4–12% sodium-dodecyl sulfate acrylamide gel electrophoresis (SDS-PAGE). Proteins were detected and measured using the LAS-4000 image analyzer (GE Healthcare) in SYBR–green fluorescence detection mode and Image Quant TL software (GE Healthcare). Protein quantification was performed as shown in Figure 2—figure supplement 1B. Average values, SD, and p-values of Welch’s t-test were calculated from three biological replicates.

Cells overexpressing FPs were collected at OD₆₆₀=1 or 2 and fixed with 100 µL of ice-cold alkaline solution (0.2 N NaOH and 0.5% β-mercaptoethanol). After 5 min of incubation on ice, 1 mL of ice-cold acetone was added to the sample and incubated at −20 °C to precipitate proteins. The protein samples were precipitated with a microfuge (15,000 rpm, 5 min), air-dried, suspended in 100 µL of SDS–PAGE sample buffer, and incubated at 65 °C for 15 min. The samples were thoroughly dissolved by sonication and subjected to SDS–PAGE (40 mA, 1 hr). After ponceau S staining, proteins are transferred to the PVDF membrane (Millipore #IPVH304F0) and blocked in 5% skim milk/TBST. Atg13 was detected using a rabbit anti-Atg13 antibody (1:3000) and peroxidase-conjugated goat anti-rabbit secondary antibody (Jackson ImmunoResearch, #111–035–003, 1:10,000). Chemiluminescent substrates (Millipore, #WBLUF0100) and Light–Capture II (ATTO) were used for signal detection.

GFP fluorescence (Ex485nm/Em535nm) was measured every 10 min using a microplate reader (Infinite F200, TECAN). Average values, SD, and p-values of Welch’s t-test were calculated from eight biological replicates.

BY4741 cells overexpressing a target protein were cultured in SC–Leu/Ura medium and harvested at the logarithmic growth phase. RNA extraction was performed according to Köhrer and Domdey, 1991. The purified RNA was quality checked by BioAnalyzer (Agilent) or MultiNA (Shimazu), and the concentration was measured by Qubit (Thermo Fisher Scientific). Purified RNA was stored at −80 °C until subsequent experiments. cDNA libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina), using half the protocol of the TruSeq RNA library prep kit. Three biological replicates were analyzed for all strains. The sequences were checked for read quality by FastP (Chen et al., 2018) and then aligned using Hisat2 (Kim et al., 2019). The aligned data were formatted into bam files by Samtools (Li et al., 2009) and quantified by StringTie (Pertea et al., 2015). Finally, expression level variation analysis was performed by EdgeR (Robinson et al., 2010). The raw data were deposited into DDBJ (accession number: PRJDB18064). The processed data (Source data 1) included the average expression levels (log10CPM) of 6685 genes in the vector control, the changes in expression (log2FC) upon overexpression of mox or mox-YG compared to the vector control, and the significance values of expression change (mox_FDR, mox-YG_FDR). Figure 3—figure supplement 1A, B is the visualization of this data using volcano plots. This data is used in subsequent data analyses below.

The strains used for the reporter assay were created by introducing the promoter region of each gene and the sequence of mScarlet-I, a type of RFP, into the FCY1 locus of the genome as shown in Figure 3—figure supplement 2A. Correct integration was checked by histidine prototrophy and 5-FC resistance of the strain. The cell density (OD₅₉₅) and RFP fluorescence (Ex535nm/Em590nm) of the created strains were measured every 10 min using a microplate reader (Infinite F200PRO, TECAN). Average and SD of max RFP fluorescence, and p-values of Welch’s t-test were calculated from eight biological replicates.

The vector control and mox-YG overexpressing cells (three biological replicates) were cultured in 5 mL of SC–LeuUra medium with shaking, and cells were collected at OD₆₆₀=1. Cells were washed twice with PBS, frozen on dry ice, and transported to Kazusa DNA Research Institute. The samples were prepared as described in the previous study (Hughes et al., 2019; Kawashima et al., 2022). The cells were dissolved in 100 mM Tris-HCl (pH 8.0) containing 4% SDS and 20 mM NaCl using BIORUPTOR BR-II (SONIC BIO Co., Kanagawa, Japan). The 20 μg of extracted proteins were quantified using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, WA, USA) at 500 ng/μL. The protein extracts were reduced with 20 mM tris (2-carboxyethyl) phosphine for 10 min at 80 °C followed by alkylation with 30 mM iodoacetamide for 30 min at room temperature in the dark. Protein purification and digestion were performed using the sample preparation (SP3) method (Kawashima et al., 2022). The tryptic digestion was performed using 500 ng/μL Trypsin/Lys-C Mix (Promega, Madison, WI, USA) for overnight at 37 °C. The digests were purified using GL-Tip SDB (GL Sciences, Tokyo, Japan) according to the manufacturer’s protocol. The peptides were dissolved again in 2% ACN containing 0.1% TFA and quantified using BCA assay at 150 ng/μL. The digested peptides were loaded directly using a 75 μm×12 cm nanoLC nano-capillary column (Nikkyo Technos Co., Ltd., Tokyo, Japan) at 40 °C and then separated with a 30 min gradient (mobile phase A=0.1% FA in water, B=0.1% FA in 80% ACN) consisting of 0 min 8% B, 30 min 70% B at a flow rate of 200 nL/min using an UltiMate 3000 RSLCnano LC system (Thermo Fisher Scientific). The eluted peptides were detected using a quadrupole Orbitrap Exploris 480 hybrid mass spectrometer (Thermo Fisher Scientific) with normal window DIA. The MS1 scan range was set as a full scan with m/z 490–745 at a mass resolution of 15,000 to set an Auto Gain Control (AGC) target for MS1 as 3×10⁶ and a maximum injection time of 23 ms. The MS2 was collected at more than m/z 200 at 30,000 resolutions to set an AGC target of 3×10⁶, maximum injection time of ‘auto’, and fixed normalized collision energy of 28%. The isolation width for MS2 was set to 4 m/z, and for the 500–740 m/z window pattern, an optimized window arrangement was used in Scaffold DIA (Proteome Software, Inc, Portland, OR, USA). The raw data were searched against an in silico predicted spectral library using DIA-NN (Demichev et al., 2020; Demichev and Chen, 2022; version:1.8.1, https://github.com/vdemichev/DiaNN). First, an in silico predicted spectral library was generated from the human protein sequence database (UniProt id UP000005640, reviewed, canonical, 20,381 entries) using DIA-NN. The DIA-NN search parameters were as follows: protease, trypsin; missed cleavages, 1; peptide length range, 7–45; precursor charge range, 2–4; precursor mass range, 495–745; fragment ion m/z range, 200–1800; mass accuracy, 10 ppm; static modification, cysteine carbamidomethylation; enabled ‘Heuristic protein interferences’, ‘Use isotopologues’, ‘MBR’, and ‘No shared spectra’. Additional commands were set as follows: ‘mass acc cal 10’, ‘peak translation’, and ‘matrix spec q’. The protein identification threshold was set at <1% for both peptide and protein false discovery rates (FDRs).

The signal intensity data for 4241 proteins (Source data 2) was used for further data analysis below.

The analysis and visualization of RNA-seq data and proteome data were conducted using custom Python code, and coding and execution were carried out in the Collaboratory. In the RNAseq data, 6685 protein-coding genes were detected (Source data 1). Among these, 4291 genes assigned to various categories of KEGG Orthology level 3 (Kanehisa et al., 2023) were placed into 77 categories. Analysis was performed on 47 categories that contained more than 10 gene elements. We investigated whether the expression of genes in each category, as a group, was significantly higher or lower upon overexpression of mox and mox-YG compared to the vector control. Specifically, mox/Vector (log2FC) and mox-YG/Vector (log2FC) were calculated along with their FDR values, and each gene was assigned to a KEGG orthology level 3 category. Next, whether the gene groups within each category showed significantly higher or lower expression changes compared to gene groups outside of their category was tested using the Mann-Whitney U-test. Significant differences were indicated in the graphs with a red border and red stripes. The graphs showed the overall distribution of genes within each category using violin plots, and only genes showing significant differences in mox/Vector (log2FC) and mox-YG/Vector (log2FC) were highlighted in swarm plots. An asterisk indicated significant differences on the graphs when the expression changes (log2FC) between mox/Vector and mox-YG/Vector were significant by the Mann-Whitney U-test. The significance threshold was set at FDR <0.05 for all. Only categories where either mox or mox-YG overexpression showed significant expression changes were extracted and presented as Figure 3B, and all 47 categories were shown in Figure 3—figure supplement 1C.

The proteome analysis was conducted using the signal intensity data (Source data 2). This data includes proteins that were detected only in some samples under certain conditions or replicates, especially those with low signals or large expression variations. Additionally, the total protein signal sum excluding mox-YG was almost the same between the vector control and mox-YG overexpression (Figure 4—figure supplement 3A). This result contradicts the SDS−PAGE findings, which indicate that the total protein mass per cell, including mox-YG, is the same between the vector control and mox-YG overexpression (Figure 4A). Therefore, it is considered necessary to adjust the total protein signal sum, including mox-YG, to match that of the vector control to understand the proteome change per cell. In this study, however, we used the most conservative approach by removing proteins with zero values and not applying any overall corrections (Figure 4—figure supplement 3B), which allowed the analysis of 3588 proteins. Although this may not correctly analyze ‘per cell’ proteome change, it enables us to capture the change in proteome composition (excluding mox-YG), thus providing information about cellular responses. If the total correction is applied, only 138 proteins were increased (Figure 4—figure supplement 3C), most of which are mitochondrial proteins.

For the detected 3588 proteins above, we compared the increase or decrease of protein groups within each category of KEGG Orthology Level 3 with those outside the category. Analysis was performed on 45 categories, each containing more than 10 proteins, to investigate whether the expression of protein groups in each category was significantly higher or lower under mox-YG overexpression compared to the vector control. Specifically, the average log2FC and its FDR values were calculated using data from three replicates for mox-YG/Vector, and each protein was assigned to a KEGG orthology level 3 category. Next, whether the protein groups within each category showed significantly higher or lower expression changes compared to protein groups outside of their category was tested using the Mann-Whitney U-test. Significant differences were indicated in the graphs with a red border and red stripes. The graphs showed the overall distribution of proteins within each category using violin plots, and only proteins showing significant differences in mox-YG/Vector (log2FC) were highlighted in swarm plots. The significance threshold was set at FDR <0.05 for all. Only categories where mox-YG overexpression showed significant expression changes were extracted and presented in Figure 4C and all 47 categories were shown in Figure 4—figure supplement 6. Published data (Gowans et al., 2018) was used to separate protein groups into rapamycin-responsive and non-responsive, and similar analysis and visualization were conducted (Figure 4—figure supplement 6A, B).

Cells overexpressing target proteins were cultured in SC–LeuUra medium and observed under an inverted microscope (DMI 6000 B, Leica) at log phase (OD₆₆₀=1.0). Images were acquired with Leica Application Suite and processed with Leica Application Suite X software (Leica Microsystems). Cells in brightfield images were separated from the images using yeast_segmentation-master_v3 (Lu et al., 2019). Cell morphological traits were assessed using parameters obtained when analyzed using CellProfiler (Ver. 4.2.6) (Carpenter et al., 2006). ‘AreaShape_Area’ was used to analyze FP-overexpressing cell size (arbitrary unit; AU) in Figure 2—figure supplement 6B, Nsr1-GFP in Figure 5—figure supplement 2C and Nsr1-mScarlet-I in Figure 5—figure supplements 5E and 6F, and Figure 6—figure supplement 7C. For analysis of the fluorescence intensity of Rip1-mScarlet-I in Figure 4—figure supplement 8B, Figure 6—figure supplement 5A. ‘Intensity_ IntegratedIntensity’ was used. ‘Intensity_MeanIntensity’ was used to analyze the fluorescence intensity of Nsr1-GFP in Figure 5—figure supplement 2D, Figure 6—figure supplement 7C. Ten images per experiment were used for analysis. The axis ratio in Figure 6—figure supplement 3 was calculated as the ratio of ‘AreaShape_MajorAxisLength’ to ‘AreaShape_MinorAxisLength’ in the data.

Cells overexpressing the target protein were collected at OD₆₆₀=1. The cells (0.05 OD unit) were treated with oxygen probe in SC–LeuUra and 50 µM antimycin A in SC–LeuUra. Oxygen consumption was measured according to the instruction manual of the Extracellular OCR Plate Assay Kit (Dojindo, #E297). The fluorescence intensity (Ex500nm/Em650 nm) was recorded at 10 min intervals using a microplate reader (Infinite F200, TECAN). The oxygen consumption rate (AU) was calculated as the slope of a linear approximation of the kinetic data.

Cells grown to log phase (OD₆₀₀=1.0) at 30 °C in SC–LeuUra medium were collected and transported to Tokai Electron Microscope Co. Transported samples were sandwiched between copper plates, flash-frozen with liquefied propane, and then dehydrated with anhydrous ethanol. Cells were fixed with a 5:5 mixture of propylene oxide and resin (Quetol-651, Nissin EM Co.). Fixed cells were cut with an ultramicrotome (Ultracut UCT, Leica) to prepare 80-nm-thick sections, stained with 2% uranyl acetate and lead stain solution (Sigma-Aldrich Co.), and observed with a transmission electron microscope (JEM-1400 Plus, JEOL Ltd.).

The strains and plasmids generated in this study are available from NBRP-yeast (https://yeast.nig.ac.jp/yeast/).