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. 2026 May 27;123(8):2213–2223. doi: 10.1002/bit.70253

Metabolic Reprogramming and Proteome Reallocation Accompany Loss of Respiratory Oscillations in Yeast Accelerostat

Gaoya Wang 1,2, Yong Chen 1, Yingping Zhuang 1,2, Guan Wang 1,2,✉
PMCID: PMC13397170  PMID: 42200525

ABSTRACT

Respiratory oscillations are a hallmark of glucose‐limited yeast chemostats, yet how growth rate shapes their emergence and collapse remains unclear. Here, we combined accelerostat cultivation with quantitative metabolomics and proteomics to characterize the transition from oscillatory to non‐oscillatory metabolism in Saccharomyces cerevisiae under aerobic, glucose‐limited conditions. Respiratory oscillations were maintained at low growth rates, attenuated at intermediate rates, and no longer observed at higher rates, coinciding with the onset of ethanol formation. Metabolomics analysis showed that oscillatory dynamics were most pronounced in tricarboxylic acid cycle intermediates and trehalose, whereas glycolysis and the pentose phosphate pathway exhibited weaker oscillations and instead adjusted pool sizes with growth rate. Quantitative proteomics further indicated that loss of oscillations was accompanied by non‐uniform proteome reallocation, including increased representation of translation, glycolysis, energy metabolism, and amino acid biosynthesis, together with reduced relative allocation to buffering and proteostasis‐related functions. Together, these results indicate a growth rate‐associated physiological transition in glucose‐limited yeast, in which the disappearance of oscillatory behavior during accelerostat cultivation is associated with a shift from respiratory to respiro‐fermentative metabolism and coordinated reorganization of the proteome.

Increasing growth rate during yeast accelerostat cultivation drives the transition from a low‐μ respiratory oscillatory state to a high‐μ ethanol‐associated non‐oscillatory state. This shift is marked by attenuation of TCA‐ and trehalose‐linked oscillatory dynamics together with proteome reallocation toward glycolysis, translation, energy metabolism, and amino acid‐associated functions.

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1. Introduction

Saccharomyces cerevisiae serves both as a canonical model for eukaryotic cell physiology and a key microbial cell factory of industrial biomanufacturing, making its metabolic dynamics highly relevant to both fundamental research and process optimization (Buziol et al. 2008; Spadiut et al. 2013; Timoumi et al. 2017; Yang et al. 2025). Under aerobic, glucose‐limited chemostat conditions, yeast cultures can exhibit sustained oscillations in gas exchange, redox state, dissolved oxygen, and central metabolite levels (Cazzador et al. 1990; Danø et al. 2002; Porro et al. 1988; Weber et al. 2012). These respiratory oscillations emerge as population‐level phenomena without external forcing and reflect coordinated metabolic dynamics across cells (Garmendia‐Torres et al. 2007; Henson et al. 2002). Beyond their physiological relevance, such oscillatory states can influence process stability and performance in continuous cultivation (Chen and McDonald 1990; Murray et al. 2007).

Previous studies have attributed yeast respiratory oscillations to interactions among glycolysis, cellular energy state, mitochondrial respiration, storage‐carbohydrate metabolism, and population‐level synchronization. Rapid feedbacks involving glycolysis, energy charge, and respiration can support self‐sustained rhythmic behavior (Aon et al. 1991; Das and Busse 1991; Ghosh et al. 1971; du Preez et al. 2012), while slower processes such as storage‐carbohydrate turnover and temporally structured biosynthetic demand shape oscillation amplitude and phase organization (Akiyama and Tsurugi 2003; Takhaveev et al. 2023; Wang et al. 2001). At the population level, diffusible metabolites can synchronize oscillations across cells, giving rise to collective metabolic dynamics (Henson et al. 2002; Richard et al. 1996). More recently, resource‐allocation perspectives have suggested that oscillatory behavior may also be associated with how cells distribute limited metabolic and proteomic capacity (Chumley et al. 2023).

Despite these advances, how respiratory oscillations change across growth regimes remains insufficiently characterized. Most studies have examined oscillations at fixed dilution rates, limiting the ability to resolve how oscillatory behavior evolves as cells transition from a low‐growth respiratory state toward the Crabtree‐positive regime. In particular, it remains unclear how oscillatory behavior attenuates and disappears, which intracellular pathways retain or lose oscillatory dynamics during this transition, and how proteome allocation is reorganized in parallel.

Here, we employed accelerostat cultivation under aerobic, glucose‐limited conditions to track S. cerevisiae across a continuous increase in growth rate. By integrating quantitative metabolomics and proteomics, we characterized the transition from oscillatory to non‐oscillatory metabolism, identified pathway‐specific patterns of oscillatory behavior, and examined how proteome allocation is reorganized as oscillations diminish.

2. Materials and Methods

2.1. Strain

All experiments were conducted using Saccharomyces cerevisiae CEN.PK113‐7D. To ensure phenotypic consistency, all cultures were derived from a single frozen master stock and subjected to only one pre‐culture step prior to inoculation.

2.2. Cultivation Media

A defined mineral medium (Chen et al. 2023) was used for all cultivations, with glucose supplied at 7.5 g/L for chemostat and accelerostat and 10 g/L for batch cultures. The medium composition per liter was: 5 g (NH4)2SO4, 3.0 g KH2PO4, 0.5 g MgSO4·7H2O, 1 mL trace metal solution, 1 mL vitamin solution.

Trace‐metal solution (per liter): 3.0 g FeSO4·7H2O, 4.5 g ZnSO4·7H2O, 4.5 g CaCl2·2H2O, 1.0 g MnCl2·4H2O, 0.3 g CoCl2·6H2O, 0.3 g CuSO4·5 H2O, 0.4 g Na2MoO4·2 H2O, 1.0 g H3BO3, 0.1 g KI, 19.0 g EDTA. Vitamin solution (per liter): 50 mg D‐Biotin, 1 g D‐Pantothenic acid Hemicalcium salt, 1 g Thiamin‐HCl, 1000 mg Pyridoxin‐HCl, 1 g Nicotinic acid, 200 mg 4‐aminobenzoic acid, and 25 g M‐lnositol.

2.3. Chemostat Cultivation

Cultivations were performed in a 5‐L bioreactor (Shanghai Guoqiang Bioengineering Equipment Co. Ltd.) operated as aerobic, glucose‐limited chemostats at a working volume of 3 L. Chemostat mode was initiated at a dilution rate of 0.10 h−1, with culture removed via an overflow line using a peristaltic pump. Operating conditions were maintained at 30°C, 1 vvm airflow (3 L/min), 600 rpm agitation. The pH was controlled at 5.0 by automated addition of 4 M NaOH (Mettler‐Toledo probe). Off‐gas O2 and CO2 fractions were continuously monitored using a mass spectrometer (Prima BT, Thermo Fisher Scientific).

2.4. Accelerostat Experiments

Accelerostat experiments were performed following establishment of a chemostat steady state at a dilution rate of 0.10 h−1. Steady state was defined operationally by stable carbon dioxide evolution rate (CER) signals and physiological parameters over consecutive residence times. After stablization, the dilution rate was increased linearly from 0.10 to 0.35 h−1 at a rate of 0.01 h−1 per hour via program‐controlled adjustment of the feed rate, resulting in a 25 h ramp. The ramp was applied at a sufficiently low rate to keep the culture near quasi‐steady state conditions (Spadiut et al. 2013). Under these conditions, the specific growth rate (μ) was assumed to approximate the imposed dilution rate. The experimental workflow thus consisted of a steady state phase followed by a continuous accelerostat ramp.

2.5. Sampling Strategy

Two complementary sampling strategies were applied during the accelerostat experiment. First, to characterize growth rate‐dependent changes, metabolomics samples were collected across the dilution‐rate ramp at predefined nominal μ values between 0.10 and 0.35 h−1. Proteomics samples were collected at predefined μ values of 0.10, 0.20, 0.30 and 0.35 h−1. Second, to resolve oscillatory dynamics, additional samples were collected based on the instantaneous CER waveform. Samples were taken at the trough, midpoint, and peak of CER cycles, enabling analysis of metabolite changes as function of both growth rate and oscillation phase.

2.6. Rapid Sampling, Quenching, and Metabolite Extraction

Extracellular metabolites were collected by fast filtration and quenched using cold steel beads and liquid nitrogen, following Wang et al. (2019b). For intracellular metabolites, approximately 1.2 mL culture was rapidly transferred into pre‐cooled ( − 27.5°C) quenching solution (40% v/v methanol) using a fast sampling device. Samples were immediately filtered, washed with cold quenching solution, and subjected to intracellular metabolite extraction using the boiling ethanol method (Li et al. 2018; Wang et al. 2019a).

2.7. Metabolomics Analysis

Intracellular metabolites, including amino acids, sugar phosphates, organic acids, and sugar alcohols were quantified by GC‐MS (Agilent 7890 A/5975 C) using isotope dilution, following Wang et al. (2019a) with column and temperature‐gradient adjustments based on Liu et al. (2019).

Extracellular glucose, ethanol, and glycerol were quantified by isocratic HPLC (Agilent 1100) using an Aminex HPX‐87H column at 50°C, with 0.01 M H2SO4 as the mobile phase at a flow rate of 0.4 mL/min.

Absolute quantification was achieved by isotope‐dilution mass spectrometry (IDMS) using uniformly‐13C‐labeled P. pastoris extracts as internal standards (Wu et al. 2005).

2.8. Proteome Sample Preparation and LC‐MS/MS Analysis

Cell pellets were resuspended in 10 volumes of 6 M guanidine hydrochloride buffer containing 100 mM Tris‐HCl (pH 8.0) and 20 mM dithiothreitol (DTT). The suspension was heated at 95°C for 10 min and subsequently disrupted by sonication using a Bioruptor (Diagenode, USA) at high power for 15 min. Mechanical lysis was further enhanced by bead beating in a FastPrep‐24 instrument (MP Biomedicals, USA) for two 30‐s cycles at 4 m/s, with cooling between cycles. Lysates were clarified by centrifugation at 17,000 × g for 10 min, and total protein concentrations were determined using the Micro‐BCA assay (Thermo Fisher Scientific).

Proteins were precipitated using a methanol/chloroform/water mixture (2:1:3, v/v/v) and the resulting pellets were dissolved in a denaturing buffer consisting of 7 M urea, 2 M thiourea, and 100 mM ammonium bicarbonate. Disulfide bonds were reduced with 2.5 mM DTT and alkylated with 5 mM iodoacetamide. Protein digestion was performed overnight using Lys‐C (Wako Pure Chemical Industries, Japan) at an enzyme‐to‐protein ratio of 1:50.

Peptides were purified using C18 reversed‐phase tips (3 M Empore, USA) and reconstituted in 0.5% trifluoroacetic acid (TFA). A 2‐µg peptide aliquot was injected into an Ultimate 3000 RSLC nano‐LC system (Dionex, USA) equipped with a C18 trap column and a 50‐cm × 75‐µm analytical emitter column (New Objective, USA) packed in‐house with 3‐µm C18 particles (Dr Maisch, Germany). The columns were equilibrated in 95% solvent A (0.1% formic acid in water) and 5% solvent B (80% acetonitrile with 0.1% formic acid).

Peptide separation was carried out using a 5%–40% solvent B gradient at a flow rate of 200 nL/min. Gradient durations were 240 min for experimental samples and 480 min for heavy‐labeled standard samples. Eluted peptides were ionized using a nano‐ESI source at 2.5 kV and analyzed on a Q Exactive Plus quadrupole‐Orbitrap mass spectrometer (Thermo Fisher Scientific).

Each run consisted of one full MS scan (350–1400 m/z, resolution 70,000 at 200 m/z, AGC target 3 × 106, maximum injection time 50 ms), followed by data‐dependent MS/MS acquisition of the ten most intense precursor ions (resolution 17,500 at 200 m/z, AGC target 5 × 104, maximum injection time 50 ms). Higher‐energy collisional dissociation (HCD) was performed with a normalized collision energy of 26. Only precursor ions with charge states +2 to +6 were selected for fragmentation.

2.9. Proteomics Data Analysis

Proteomic changes were assessed from three biological replicates. Significance testing used two‐sided t‐tests on log2‐transformed abundances; proteins with p < 0.05 and |log2FC | > 1 were considered as differentially abundant. Gene Ontology and KEGG enrichment analyses were performed using Metascape (Zhou et al. 2019) with Benjamini–Hochberg adjusted p < 0.05.

Proteome abundance data obtained at four nominal growth‐rate points (μ = 0.10, 0.20, 0.30, and 0.35 h−1) were subjected to trend analysis based on the abundance trajectories of individual proteins across the accelerostat ramp. For each quantified protein, the mean abundance at each nominal μ point was first calculated from three biological replicates and log2‐transformed before analysis. Proteins were then grouped according to the direction and shape of their abundance changes across the four sampled conditions.

GO and KEGG enrichment analyses were performed separately for proteins using Metascape (Zhou et al. 2019). Protein identifiers were mapped to the corresponding S. cerevisiae gene annotations and analyzed against the yeast background proteome. Enriched terms were considered significant at Benjamini‐Hochberg adjusted p < 0.05. Only significantly enriched GO biological process terms and KEGG pathways were retained for interpretation and visualization. The enrichment results were used to summarize the dominant biological functions represented.

For pathway‐level proteome allocation analysis, proteins were assigned to functional categories according to the annotation schemes of Björkeroth et al. (2020) and Xia et al. (2022). The relative proteome allocation of each category at a given nominal μ was calculated as the summed abundance of proteins within that category divided by the summed abundance of all quantified proteins at the same condition.

3. Results and Discussion

3.1. Accelerostat Growth Reveals Growth Rate‐Dependent Respiratory Oscillations in Saccharomyces cerevisiae

A reference chemostat at a dilution rate of 0.10 h−1, at which physiological parameters were consistent with canonical glucose‐limited behavior previously reported for S. cerevisiae (Xia et al. 2022) (Table S1). Continuous off‐gas analysis provided a high‐resolution readout of respiratory activity via the carbon dioxide evolution rate (CER; Figure 1A). Under these conditions, the culture exhibited robust multi‐hour CER oscillations with a period of approximately 3–4 h. Such oscillatory dynamics are characteristic of glucose‐limited yeast cultures operating below the Crabtree threshold, where extracellular ethanol concentrations remain low (0.002–0.085 g L−1) (Chen and McDonald 1990; Porro et al. 1988).

Figure 1.

Figure 1

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Growth‐rate‐dependent dynamics of respiratory oscillations. (A) Carbon dioxide evolution rate (CER, black line) and specific growth rate (μ, green line) under steady‐state chemostat conditions at D = 0.10 h−1. (B) CER dynamics (black line) during the dilution‐rate ramp experiment, in which the dilution rate (green line) was increased linearly from 0.10 to 0.35 h−1. Sampling points are indicated by red dots. Phase I corresponds to the steady‐state oscillatory regime, Phase II to the oscillatory regime during the ramp, and Phase III to the non‐oscillatory regime after oscillation collapse. (C) Changes in extracellular concentration of glucose, glycerol, and ethanol at different specific growth rate in accelerostat experiment. The dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD.

At a higher dilution rate (D = 0.35 h−1), close to the maximal specific growth rate of the strain (μmax ≈ 0.40 h−1), oscillatory CER behavior was no longer observed (Figure S1 and Table S2), consistent with previous studies showing that increasing dilution rate alters oscillatory regimes (Beuse et al. 1998).

To resolve the transition continuously, a linear dilution‐rate ramp (accelerostat cultivation) from 0.10 to 0.35 h−1 was applied while monitoring CER and sampling for metabolomic and proteomic analysis (Figure 1B). Based on CER waveform characteristics, the accelerostat trajectory can be divided into three phases (Figure 1B ). In Phase I, oscillations were stable and comparable to those observed at D = 0.10 h−1. In Phase II, oscillations persisted but exhibited reduced amplitude and increased frequency. In Phase III, a distinct oscillatory pattern was no longer evident, and CER increased more monotonically with dilution rate.

The transition from oscillatory to non‐oscillatory behavior occurred near μ ≈ 0.28–0.30 h−1 (Figure 1A,B) and coincided with the onset of detectable extracellular ethanol (Figure 1C). Together, these observations indicate that respiratory oscillations are confined to a low growth‐rate regime under the present conditions and become progressively attenuated as μ increases.

3.2. Extracellular Metabolic Signatures Associated with Respiratory Oscillations

Extracellular metabolite dynamics were analyzed during the accelerostat experiment to characterize changes associated with the transition from oscillatory to non‐oscillatory behavior. Within individual oscillation cycles, extracellular glucose exhibited a reproducible rise–fall profile (Figure 2; Figure S3). Glucose concentrations increased transiently early in each cycle and declined as CER approached its peak. Over longer timescales, net glucose levels remained relatively constant, consistent with balanced glucose uptake rates estimated from steady‐state q‐value calculations (Figure 1C). Other extracellular intermediates of the glycolytic pathway remained near or below detection limits, indicating minimal accumulation of upstream metabolites.

Figure 2.

Figure 2

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Extracellular metabolite dynamics across growth‐rate regimes. Extracellular metabolite concentrations measured across different phases of the dilution‐rate ramp. Dashed vertical lines indicate phase boundaries. Data represent mean ± SD from n = 3 biologically independent samples.

In contrast, several extracellular tricarboxylic acid (TCA) cycle intermediates displayed more pronounced long‐term trends. Citrate, fumarate, and malate exhibited limited within‐cycle variation but decreased progessively across successive cycles (Figure 2 and Figure S3). α‐Ketoglutarate and succinate showed modest phase‐dependent modulation within individual cycles while also declining over the course of the ramp (Figure 2 and Figure S3). Compared with the short‐term oscillatory behavior of glucose, these TCA‐related metabolites primarily reflected a slower, growth‐associated shift in extracellular metabolism. This pattern suggests that the contribution of respiratory metabolism to the extracellular metabolite profile decreases as cultures transition toward the non‐oscillatory regime. Similar trends accompanying reduced respiratory oscillations have been reported in previous chemostat and fermentation studies (Aon et al. 1991; Lloyd et al. 2003; Wang et al. 2013; Zhang et al. 2021).

3.3. Intracellular Metabolites Display Distinct Oscillatory Dynamics during Respiratory Oscillations

Intracellular metabolite profiles were analyzed to identify pathway‐specific dynamic responses during the accelerostat transition (Figure 3; Figures S4–5 ). Upper glycolytic intermediates showed moderate but reproducible oscillatory behavior. Intracellular glucose level remained relatively constant across cycles, whereas glucose‐6‐phosphate (G6P) and fructose‐6‐phosphate (F6P) exhibited reproducible down–up trajectories within individual cycle and increased modestly with growth rate (Figure 3A and Figures S4–5 ). These patterns indicate that upper glycolytic metabolites remain responsive to oscillatory changes while their average pool sizes shift gradually along the growth‐rate ramp. Previous studies have reported stronger oscillatory behavior for downstream glycolytic intermediates in other oscillatory contexts, suggesting that different glycolytic nodes may contribute differently depending on system conditions (Wittmann et al. 2005).

Figure 3.

Figure 3

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Intracellular metabolite dynamics across growth‐rate regimes. Intracellular mebalites concentrations measured across different cycles of the accelerostat experiment. Intracellular metabolites were grouped into glycolysis (A), TCA cycle (B), PPP (C), and sugar alcohols (D). Dashed vertical lines indicate cycle boundaries. Data represent mean ± SD from n = 3 biologically independent samples.

Figure 5.

Figure 5

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Remodeling of intracellular amino acid pools across growth‐rate regimes. Intracellular amino acid concentrations measured across different phases of the dilution‐rate ramp. Amino acids are grouped according to their metabolic precursors (3‐phosphoglycerate, pyruvate, α‐ketoglutarate, and oxaloacetate). Dashed vertical lines indicate phase boundaries. Data represent mean ± SD from n = 3 biologically independent samples.

In contrast, intermediates of the pentose phosphate pathway (PPP), including 6‐phosphogluconate, ribose‐5‐phosphate and sedoheptulose‐7‐phosphate, showed minimal within‐cycle variation but increased steadily across successive cycles (Figure 3B; Figures S4–5), indicating a predominantly growth‐associated adjustment in pool size rather than pronounced oscillatory behavior. ntracellular TCA intermediates exhibited more pronounced oscillatory dynamics, with clear up–down trajectories that were closely aligned with CER oscillations (Figure 3C; Figure S4‐S5). These observations are consistent with dynamic imbalances between carbon flux into TCA cycle and respiratory capacity, leading to periodic changes in organic acids. Previous studies have shown that perturbations in mitochondrial respiration or NADH reoxidation can alter or suppress oscillatory behavior, underscoring the importance of respiratory processes in shaping oscillatory regimes (Ghosh and Chance 1964; Olsen et al. 2009).

Among all intracellular metabolites, trehalose displayed the largest oscillatory amplitudes (Figure 3D; Figure S4‐S5) and declined progressively with increasing μ, reaching low levels near the point at which CER oscillations disappeared. Given its established roles as a dynamic carbon and energy reserve, this pattern is consistent with a reduced contribution of trehalose‐associated buffering in the non‐oscillatory regime (Aon et al. 1991; Blázquez et al. 1993).

Cycle‐resolved analysis further showed that oscillation amplitudes generally decreased with increasing μ, most clearly for TCA‐associated intermediates and trehalose (Figure 4; Figure S6). Taken together, these results indicate that intracellular metabolites contribute unequally to the oscillatory phenotype. Glycolytic and PPP metabolites primarily exhibit gradual changes in pool size, whereas TCA intermediates and trehalose display the most pronounced oscillatory behavior and the strongest attenuated as the culture approaches the non‐oscillatory state.

Figure 4.

Figure 4

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Growth‐rate‐dependent attenuation of oscillation amplitudes. For each oscillatory cycle, measurements from different sampling points were averaged, and the cycle‐level amplitude was calculated as the difference between the maximum and minimum values within that cycle.

3.4. Remodeling of Intracellular Amino Acid Pools during Respiratory Oscillations

To examine whether the oscillatory to non‐oscillatory transition extends into biosynthetic metabolism, intracellular amino acid pools were analyzed by grouping metabolites according to their metabolic precursors (Figure 5; Figure S7). Amino acid pools did not respond uniformly during the accelerostat transition. Instead, both the direction and amplitude of changes varied by precursor class, indicating that pathway‐dependent propagation of oscillatory dynamics into amino acid metabolism. Similar heterogeneous responses of free intracellular amino acid pools can display distinct rhythmic responses during autonomous oscillations in S. cerevisiae (Hans et al. 2003).

Amino acids derived from 3‐phosphoglycerate (3PG) and erythrose‐4‐phosphate (E4P), including serine, glycine, phenylalanine, and tyrosine, exhibited distinct within‐cycle trajectories (Figure 5). Serine, glycine and tyrosine followed reproducible up–down patterns, whereas phenylalanine exhibited a down–up trajectory. These differences are consistent with their distinct precursor dependencies, with serine and glycine more directly linked to glycolytic flux and phenylalanine and tyrosine relying on PPP‐derived precursors. Across the ramp, several of these amino acids also decreased progressively, consistent with changes in precursor availability and biosynthetic demand during growth‐rate increase (Ducker and Rabinowitz 2017).

Pyruvate‐derived amino acids, including alanine, valine, leucine, and isoleucine, exhibited coordinated behavior, with synchronized up–down trajectories that closely followed pyruvate dynamics (Figure 5). Alanine and valine increased across the ramp, whereas isoleucine exhibited more limited variation, consistent with previous observations in oscillatory systems (Hans et al. 2003).

Amino acids derived from α‐ketoglutarate (α‐KG), including glutamate, glutamine, proline, and lysine, displayed pronounced oscillatory patterns (Figure 5). Glutamate, glutamine, and proline followed up–down trajectories, whereas lysine showed a complementary down–up pattern. These patterns are consistent with dynamic redistribution of carbon and nitrogen within α‐KG‐associated metabolic branches, in line with the central role of glutamate and glutamine metabolism in nitrogen assimilation (Magasanik and Kaiser 2002; Mara et al. 2018). Notably, the average levels of these amino acids increased across the ramp, suggesting increased investment in nitrogenassociated biosynthesis at higher growth rate.

For oxaloacetate‐derived amino acids, aspartate showed a characteristic down–up trajectory with asparagine showing a similar pattern and threonine displaying more limited variation (Figure 5). Compared with the α‐KG‐derived group, oscillatory behavior in this branch was less pronounced, indicating a weaker propagation of oscillatory dynamics.

Taken together, these results demonstrate that intracellular amino acid pools are remodeled in a pathway‐specific manner during the accelerostat transition. Oscillatory dynamics were most evident in amino acids linked to pyruvate, α‐KG, and oxaloacetate metabolism, whereas PPP‐associated branches primarily showed gradual changes in pool size.

3.5. Proteome Reallocation and Functional Implications of Oscillation Loss at High Growth Rate

To determine whether the metabolic transition observed along the accelerostat ramp was accompanied by systematic changes in proteome allocation, quantitative proteomic data were analyzed across four nominal growth‐rate points (μ = 0.10, 0.20, 0.30, and 0.35 h−1). Proteins were first grouped according to their abundance trajectories along the ramp, resulting in eight distinct trends (Figure S8). Two representative monotonic clusters were selected for further analysis: a progressively increasing cluster (Cluster A, corresponding to Cluster 2 in Figure S8) and a progressively decreasing cluster (Cluster B, corresponding to Cluster 4 in Figure S8). Functional enrichment analyses revealed that Cluster A was associated with ribosomal proteins, purine metabolism, glycolysis, pyruvate metabolism, the TCA cycle, oxidative phosphorylation, and proteasomal components (Figure 6A). In contrast, Cluster B was enriched in ribosome biogenesis, nucleocytoplasmic transport, peroxisomal functions, autophagy, and trehalose metabolism.

Figure 6.

Figure 6

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Proteome reallocation underlying the loss of respiratory oscillations. (A) KEGG and GO enrichment analysis of proteins exhibiting two representative dynamic trends across the dilution‐rate ramp. Enrichment analyses were performed separately for each cluster, and only significantly enriched terms (FDR < 0.05) are shown. (B–C) Proteome allocation patterns across different specific growth rates. Data represent mean ± SD from n = 3 biologically independent samples.

To complement the trend‐based analysis, pathway‐level proteome allocation was evaluated using established functional classification schemes of Björkeroth et. al (2020) and Xia et. al (2022). Among non‐metabolic functions, translation‐associated proteins increased progressively with μ, whereas transcription‐ and ER‐associated functions decreased at μ = 0.20 h−1 and then remained relatively stable (Figure 6B; Figure S9). Nucleotide‐associated functions showed limited variation, while stress‐associated functions remained relatively constant up to μ = 0.30 h−1 and increased at μ = 0.35 h−1. These patterns indicate that non‐metabolic processes exhibited distinct, non‐uniform responses across the growth‐rate range.

Within metabolism‐associated categories, glycolysis, energy metabolism, and amino acid metabolism increased progressively with μ (Figure 6C and Figure S9). By contrast, chaperone‐associated functions declined and plateaued at high growth rate, while mitochondria‐associated functions showed a biphasic pattern, decreasing at intermediate μ and increasing again at higher μ. Lipid‐related functions increased initially and then declined. Together, these observations indicate a growth‐associated redistribution of proteome allocation across functional categories, with increased representation of biosynthetic and carbon‐processing pathways and reduced relative allocation to selected proteostasis‐related functions.

At the system level, the loss of respiratory oscillations coincided with these changes in proteome organization. Oscillatory states have been associated with redox balancing, temporal coordination of biosynthetic proceses, and metabolic robustness (Murray et al. 2007; Takhaveev et al. 2023). In the present dataset, the transition toward a non‐oscillatory regime was accompanied by reduced relative representation of trehalose‐associated and proteostasis‐related functions together with increased abundance of pathways linked to central carbon metabolism. These patterns are consistent with a shift toward a high‐growth metabolic state characterized by increased carbon throughput and altered buffering capacity.

Such changes may also be relevant for continuous bioprocess performance. Previous studies have shown that oscillatory operation can influence fermentation efficiency and stability, including in very‐high‐gravity ethanol production systems (Shen et al. 2010; Zhang et al. 2021). The present results extend these observations by linking the disappearance of oscillatory behavior to coordinated changes in proteome allocation, suggesting that control of oscillatory regimes may affect both metabolic state and process robustness.

Taken together, these results show that the loss of respiratory oscillations during accelerostat growth is accompanied by non‐uniform proteome reallocation. As μ increases, functions associated with biosynthesis and fermentative metabolism become more prominent, whereas selected buffering‐ and proteostasis‐related functions are relatively reduced.

4. Conclusion

In aerobic, glucose‐limited accelerostat cultures of Saccharomyces cerevisiae, respiratory oscillations were confined to a defined low growth‐rate window below μ ≈ 0.30 h−1. As μ increased, CER oscillations were progressively attenuated and disappeared near the onset of detectable extracellular ethanol. Metabolomics analysis showed that the strongest oscillatory behavior was associated with TCA‐cycle intermediates and trehalose, whereas glycolytic and PPP metabolites primarily exhibited gradual changes in pool size. Quantitative proteomics further indicated that the transition from oscillatory to non‐oscillatory behavior was accompanied by non‐uniform proteome reallocation, including increased representation of translation, glycolysis, energy metabolism, and amino acid biosynthesis, together with reduced relative allocation in buffering and proteostasis‐related functions. Taken together, these results indicate that the loss of respiratory oscillations during accelerostat growth is associated with coordinated metabolic and proteomic reorganization.

Author Contributions

G.Y.W. and G.W. designed research; G.Y.W. and Yong Chen performed research; Y.P.Z. provided critical comments; G.Y.W. and G.W. wrote the paper.

Supporting information

Figure S1: CER under steady‐state chemostat conditions at D = 0.35 h−1. Figure S2: Dry cell weight dynamics during the accelerostat experiment. Dry cell weight was monitored throughout the 25 h accelerostat ramp, during which the dilution rate was increased linearly from 0.10 to 0.35 h−1. Biomass concentration remained nearly unchanged across the ramp, indicating quasi‐steady‐state behavior during the transition. Data represent mean ± SD from n = 3 biologically independent samples. Figure S3: Extracellular metabolite concentrations over time under Ramp condition. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S4: Intracellular metabolite dynamics across growth‐rate regimes. Intracellular metabolite concentrations measured across different phases of the dilution‐rate ramp. Dashed vertical lines indicate phase boundaries. Data represent mean ± SD from n = 3 biologically independent samples. Figure S5: Intracellular central carbon metabolite concentrations over time. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S6: Variation plot of mean values across different cycles. For each cycle, data from all sampling times were averaged to obtain a cycle‐level mean, and these means were then compared across cycles. Figure S7: Intracellular amino acid concentration changes over time. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S8: Results of trend‐analysis for proteome allocation dynamics under different specific growth rates. Figure S9: Functional proteome allocation at different dilution rates. Relative proteome allocation across functional categories at different nominal dilution rates during the accelerostat experiment. Values represent the fraction of total quantified proteome assigned to each category. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Table S1: Key physiological parameters during aerobic chemostat cultures at μ = 0.1 h−1. Values are mean ± SD from biological triplicates (n = 3). Table S2: Key physiological parameters during aerobic chemostat cultures at μ = 0.35 h−1. Values are mean ± SD from biological triplicates (n = 3).

BIT-123-2213-s001.docx (27.5MB, docx)

Acknowledgments

This work was financially supported by the National Key R&D Program of China (2024YFA0920300), Natural Science Foundation of China (22578122), Natural Science Foundation of Shanghai (25ZR1402110), the Taishan Scholars Program of Shandong Province (tspn202408281), the Explorers Program of Shanghai (Basic Research Funding) (25TS1401100) and the Jiangsu Provincial Major Science and Technology Special Project (BG2025044).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. Raw data can be made available upon reasonable request. The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD070628.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: CER under steady‐state chemostat conditions at D = 0.35 h−1. Figure S2: Dry cell weight dynamics during the accelerostat experiment. Dry cell weight was monitored throughout the 25 h accelerostat ramp, during which the dilution rate was increased linearly from 0.10 to 0.35 h−1. Biomass concentration remained nearly unchanged across the ramp, indicating quasi‐steady‐state behavior during the transition. Data represent mean ± SD from n = 3 biologically independent samples. Figure S3: Extracellular metabolite concentrations over time under Ramp condition. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S4: Intracellular metabolite dynamics across growth‐rate regimes. Intracellular metabolite concentrations measured across different phases of the dilution‐rate ramp. Dashed vertical lines indicate phase boundaries. Data represent mean ± SD from n = 3 biologically independent samples. Figure S5: Intracellular central carbon metabolite concentrations over time. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S6: Variation plot of mean values across different cycles. For each cycle, data from all sampling times were averaged to obtain a cycle‐level mean, and these means were then compared across cycles. Figure S7: Intracellular amino acid concentration changes over time. The black dashed line indicated the point at which the Ramp perturbation was imposed starting from the steady state at D = 0.10 h−1, while the red dashed line marked the onset of ethanol production, indicating the emergence of the Crabtree effect. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Figure S8: Results of trend‐analysis for proteome allocation dynamics under different specific growth rates. Figure S9: Functional proteome allocation at different dilution rates. Relative proteome allocation across functional categories at different nominal dilution rates during the accelerostat experiment. Values represent the fraction of total quantified proteome assigned to each category. Error bars derived from n = 3 biologically independent samples and data were presented as mean ± SD. Table S1: Key physiological parameters during aerobic chemostat cultures at μ = 0.1 h−1. Values are mean ± SD from biological triplicates (n = 3). Table S2: Key physiological parameters during aerobic chemostat cultures at μ = 0.35 h−1. Values are mean ± SD from biological triplicates (n = 3).

BIT-123-2213-s001.docx (27.5MB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. Raw data can be made available upon reasonable request. The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD070628.

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