Time-Resolved Proteomics Analysis of the Cell Wall-Enriched Fraction in Saccharomyces cerevisiae S288c during Batch Fermentation

Abstract

The yeast cell wall (YCW) is a dynamic and multifunctional structure that is essential for cellular integrity, stress resistance, and interactions with the surrounding environment. Its structural composition is strongly influenced by external factors, such as temperature, pH, and nutrients. In batch cultures, yeast grows in a closed system with limited nutrients, resulting in distinct growth phases that are associated with major metabolic transitions. Although the global proteomic changes during growth kinetics have been described, the temporal changes of cell wall protein (CWP) expression remain poorly understood. In this study, we investigated the kinetics of protein expression in the cell wall-enriched fraction of Saccharomyces cerevisiae S288c during a bioreactor-based batch cultivation in rich medium. A classical proteomics workflow was applied to capture the dynamic variation of protein expression at multiple time points in these fractions, with a focus on covalently linked CWPs (cCWPs). The results revealed qualitative and quantitative changes in cCWP expression correlated with metabolic shifts between growth phases. Proteins involved in cell wall remodeling and glycoprotein biosynthesis were particularly enriched at the initial sampling point, corresponding to the transition from flask cultivation to bioreactor conditions, when the overall cCWP abundance was the highest. Time-resolved quantitative proteomics, transcription factor analysis, and functional enrichment indicated the coordinated regulation of cell wall adaptation. Stationary phase-specific protein markers associated with glucose depletion were identified, providing insight into nutrient-limited cell wall remodeling.

Introduction

Saccharomyces cerevisiae has been a valuable microorganism throughout human history, playing a central role in food and beverage fermentation for thousands of years. Since the complete sequencing of its genome in the late 20th century, and thanks to its well-characterized physiology and long-standing record of safe use, the applications of S. cerevisiae have expanded far beyond traditional fermentation. − Today, it is also a key player in diverse and advanced biotechnological processes. , In addition to its applied importance, S. cerevisiae has profoundly advanced our understanding of eukaryotic biology, serving as a simple, cost-effective, and genetically tractable model organism. ,, Its versatility stems from its well-defined cellular structures and metabolic pathways, which can be exploited in both native and genetically modified forms. Central to all its functions is metabolism, the transformation of nutrients into energy and essential biomolecules for growth and survival. Consequently, the study of S. cerevisiae’s nutritional requirements is fundamental to understanding its physiology, genetics, and industrial potential. Insights into yeast nutrition are crucial for optimizing growth, regulating metabolic activity, improving stress responses, and designing defined media and controlled culture conditions. In addition to the consideration of culture medium composition, the culture medium feeding strategy is also crucial for S. cerevisiae yeast growth. There are four major culture medium feeding strategies: discontinuous or “batch”, semicontinuous or “fed-batch”, continuous, and perfusion. ,

In batch culture, all nutrients required for cell growth are initially provided in the medium, and the culture is initiated by inoculating biomass from a seed reactor. The system is closed, with no addition or renewal of nutrients during the process. Cell growth varies over time, driven by nutrient availability and consumption and concomitant metabolic shifts. After an initial lag phase (during which cells adapt to the new environment), growth enters an exponential (log) phase, which slows down at the diauxic shift as preferred nutrients (e.g., glucose) become depleted. This is followed by the stationary phase, where growth ceases and a balance between cell division and death is reached. Eventually, the culture enters the death phase, driven by nutrient exhaustion and accumulation of waste products. Once nutrients are fully consumed, the process halts, inherently limiting both productivity and scalability for industrial applications. ,, These dynamic physiological transitions are closely linked to changes at multiple omics levels, including the transcriptome, , proteome, − and metabolome, , each reflecting the distinct phases of growth under defined culture conditions. Recent advances have leveraged machine learning and high-dimensional proteomics data to construct dynamic models that capture yeast behavior over time across various culture environments. , These integrated approaches not only account for uncertainty but also identify key proteins most relevant to growth dynamics.

Previous work has demonstrated that the biochemical composition of the YCW is subject to culture-dependent remodeling, underscoring its dynamic nature. Our interest in this organelle stems from its critical role as a rigid barrier that both protects the cell and mediates interactions with the external environment. , Moreover, the YCW is composed of diverse biomolecules, such as mannoproteins, β-glucans, and chitin, that are functionally and structurally valuable for a wide range of applications. Although several previous studies have examined the temporal dynamics of the S. cerevisiae proteome under various culture conditions, ,,− including organelle-specific analyses, ,, none has specifically focused on the dynamics of cell wall associated proteins. The temporal evolution of S. cerevisiae covalently linked cCWP during batch culture has not, to our knowledge, been systematically described. In the early 2000s, Yin and colleagues conducted proteomic analyses to identify and quantify CWPs in S. cerevisiae strains grown in flask-based batch cultures, employing both label-free and label-based approaches. In their work, Yin and co-workers identified and quantified 19 covalently linked CWPs that were consistently detected across all growth phases following proteolytic digestion directly or subsequently to chemical or enzymatic fractionation of YCW isolated by mechanical disruption. In their study, Pir3p was uniquely observed during the stationary phase of wild type and mutant strains of FY833 cultivated in batch mode in YPD medium within flasks. However, mass spectrometry technologies have since advanced substantially, allowing for far more comprehensive and sensitive detection of proteins. These developments underscore the importance of revisiting such analyses using modern instrumentation and strictly controlled conditions, as enabled by bioreactor systems.

This study aims to investigate the dynamic change of covalently linked CWP expression during a batch culture in YCW-enriched fractions. To this end, we used firmly regulated bioreactors and carried out experiments in biological triplicates with well-described sample preparation methods, yielding a high reproducibility and precision. The model organism used was S. cerevisiae strain S288c, cultivated in a rich medium. Cell samples were collected at key growth phases - lag phase, exponential (log) phase, diauxic shift, and stationary phase - for YCW extraction. The extracted fractions were then analyzed using label-free bottom-up quantitative proteomics to profile dynamic changes in the expression of cell-wall-associated proteins over time.

Experimental Procedures

Yeast Culture Conditions

The S. cerevisiae S288c strain (ATCC 204508) was grown for 24 h at 30 °C in 150 mL of YPD medium in a 250 mL Erlenmeyer flask with shaking at 120 rpm. After centrifugation and washing, a yeast suspension (5 g dry weight/L) was prepared, and 500 μL was transferred to 3 L of sterile YPD medium and incubated for 16 h at 30 °C. The resulting culture was centrifuged and washed to obtain a yeast cream concentrated at 8 g/L. A 7 L bioreactor containing 5.3 L of sterile YPD medium was inoculated with 0.45 g dry yeast. Batch cultures were performed in triplicate for 24 h at 30 °C with an airflow of 1 VVM and pH maintained at 5.0 by automatic addition of acid and base. Cell growth was monitored by OD600 measurements. Glucose and ethanol concentrations in filtered culture supernatants were determined by HPLC using an Aminex HPX-87H column. Samples (1.25 g dry matter) were collected at 0, 4, 7.5, 9, and 24 h, corresponding to the seed, early exponential, early diauxic, late diauxic, and stationary phases, respectively.

Isolation of the YCW-Enriched Fraction

YCW isolation by mechanical disintegration was carried out as described previously. Yeast cells were resuspended at 400 mg·mL^–1 in ice-cold lysis buffer (10 mM Tris–HCl, pH 7.5, 1× cOmplete protease inhibitor cocktail; Roche Diagnostics, Germany) and transferred to BeadBug tubes prefilled with 0.5 mm glass beads (Benchmark Scientific, USA). Cells were lysed using 15 cycles of 1 min homogenization followed by 5 min cooling on ice. Lysates were separated by centrifugation (1000g, 5 min) from beads, which were washed three times with 1 M NaCl. The lysate and washes were combined and centrifuged (4000g, 10 min, 4 °C; Eppendorf 5430 R). The resulting pellet was washed three times with 1 M NaCl to reduce cytosolic contamination, then extracted twice with boiling SDS buffer (2% SDS, 100 mM β-mercaptoethanol, 100 mM EDTA, 150 mM NaCl, 50 mM Tris–HCl, pH 7.5) for 10 min each. This extraction removed the non-covalently bound CWPs. After centrifugation (20 000g, 5 min; Beckman Coulter Allegra 64R), the pellet was washed 5 times with ultrapure water to obtain YCW isolates and were dried under vacuum (Eppendorf Concentrator Savant ISS110).

Sample Preparation for Proteomics Analysis

Protein concentration in YCW-enriched fractions was determined using a Pierce Bicinchoninic acid protein assay kit (Thermo Scientific). 50 μg of proteins from each sample was digested overnight at 37 °C with trypsin (1 μg, V5111, Promega) using eFASP method in filtration devices (Amicon 10 kDa MWCO, Millipore). Peptides were recovered and extracted three times with ethyl acetate (270989; Sigma-Aldrich, Saint-Louis, MO, USA) and dried in Eppendorf Concentrator plus. Peptide concentration was determined after adding 10 μL of 0.1% formic acid using the absorbance measurement at 214 nm with a spectrophotometer (Denovix DS-11 + spectrophotometer; Denovix Inc., USA).

LC-MS/MS Analysis

Peptides (1 μg) were analyzed using a nanoflow HPLC system (U3000 RSLC, Thermo Fisher Scientific) coupled online to a Q Exactive Plus mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray ion source. Samples were loaded onto a preconcentration trap (Acclaim PepMap100 C18, 5 μm, 300 μm i.d. × 5 mm, Thermo Scientific) via partial loop injection for 5 min at 10 μL·min^–1 using buffer A (5% acetonitrile, 0.1% formic acid). Separation was performed on a reversed-phase analytical column (Acclaim PepMap100 C18, 3 μm, 75 μm i.d. × 500 mm) with a linear gradient from 5% to 50% buffer B (75% acetonitrile, 0.1% formic acid) at a flow rate of 250 nL·min^–1 and 45 °C. The gradient lasted 160 min, followed by a 10 min wash with 99% buffer B and re-equilibration with buffer A. The total LC-MS/MS runtime was approximately 180 min.

Data Processing

Peptide sequencing and protein identification were performed using Proteome Discoverer 2.2 (Thermo Scientific) with the Sequest search engine against the S. cerevisiae S288C data set from SGD. Peptide mass tolerance were set to 10 ppm for precursors and 0.01 Da for fragments. Variable modifications included methionine oxidation and asparagine deamidation. Protein abundances were normalized and statistically analyzed in Perseus (v2.1.5.0). Proteins with a log₂ fold change > 1.5 and p < 0.05 (t-test or ANOVA with Benjamini–Hochberg correction) were considered differentially expressed. GO enrichment focused on cellular components using UniProtKB and SGD GO Slim, complemented by an in-house yeast cell wall protein (CWP) database. Transcription factor associations were analyzed with YEASTRACT+ (“Rank by TF”), , and Venn diagrams were generated using jvenn and Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). Protein networks were analyzed using String DB. ^, CWP expression heatmaps, hierarchical clustering (Euclidean distance), and correlation analyses (Spearman) were performed using Morpheus (https://software.broadinstitute.org/morpheus) using z-score normalization (Median and Median Absolute Deviation). Differential proteomics ofS. cerevisiae batch cultures in rich medium were performed using three independent biological replicates. Samples were collected at identical time points and processed in parallel. Proteins were analyzed with Perseus (v.2.1.5.0) and retained if detected in at least two replicates. Abundances were normalized based on total intensities, and differential expression was assessed using Student’s t-test and ANOVA with Benjamini–Hochberg correction, applying thresholds of log₂ fold change > 1.5 and p < 0.05.

Results

Identification and Quantification of Proteins in Batch Bioreactor Culture

The S. cerevisiae S288c reference strain was specifically chosen for its well-characterized genome and the comprehensive omics data available, thus facilitating data integration and interpretation. Batch cultures were sampled across all growth phases, spanning from proliferation to stationary-phase arrest (Supporting Information Figure S1). Sampling time points correspond to early exponential phase (b-T4h), early diauxic phase (b-T7.5h), late diauxic phase (b-T9h), and stationary phase (b-T24h), in addition to a sample from the seed that corresponds to the beginning of bioreactor culture (b-T0h). To ensure the high data representativeness and robustness and to capture biologically relevant changes, independent batch fermentations were conducted in triplicate bioreactors under strictly controlled environmental conditions, including oxygen availability, pH, and temperature. Indeed, this robustness is evidenced by a Pearson correlation coefficient r greater than 0.8 between the replicates across all conditions (Supporting Information Table S1). Under aerobic conditions, it is well-known and established that glucose-grown cultures of S. cerevisiae engage in both respiration and fermentation, resulting in the accumulation of ethanol and other fermentation products. This is followed by a diauxic growth phase, where these metabolites are subsequently respired until the stationary phase is reached. This is clearly shown in the growth curve (Supporting Information Figure S1A) as well as glucose consumption and ethanol production profiles in our study (Supporting Information Figure S1B).

Following YCW-enriched fraction extraction and trypsin digestion, peptide samples were analyzed by a bottom-up label-free quantitative proteomics approach using a 3 h LC gradient. Over the course of batch fermentation, the number of quantified proteins slightly increased from an average of 950 at b-T0h to 1038 at b-T24h (Figure A). A protein was considered quantified at a given growth phase if it was detected in at least two replicates under the same conditions. The sets of quantified proteins were compared across all growth phases (Figure B) using the jvenn tool. When combined, all data sets yield a matrix comprising a total of 1558 unique quantified proteins. As illustrated in the Venn diagram (Figure B), 410 proteins were common to all phases, while 544 proteins were unique to specific phases, primarily at b-T0h (274 proteins) and b-T24h (183 proteins). These findings indicate that the proteomes at the start and end of fermentation are more distinct from those observed during the active growth phase. This observation is further supported by the correlation matrix presented in Supporting Information Table S1. The proteomic profile of the late diauxic phase (b-T9h) shows moderate correlation with both the early exponential phase (b-T4h) and the early diauxic shift (b-T7.5h), with correlation coefficients ranging from 0.6 to 0.7. Notably, b-T0h exhibits the weakest correlation with all other phases (correlation coefficient of <0.6) and the highest variation. In contrast, the early diauxic shift phase (b-T7.5h) correlates strongly with both the early exponential (b-T4h) and late diauxic (b-T9h) phases, with coefficients exceeding 0.8. Additionally, the late diauxic phase (b-T9h) also shows a good correlation (coefficients between 0.75 and 0.8) with both the early exponential (b-T4h) and stationary (b-T24h) phases. To gain insights into their cellular localization, we analyzed the Gene Ontology (GO) annotations of the top 100 most abundant proteins quantified in each replicate and condition. These proteins account for over 80% of the total protein abundance (data not shown), and their distribution (in terms of relative abundance) is presented in Figure C. We observed an increase in the abundance of mitochondrial proteins (from 5.4% at b-T0h to 17% at b-T24h) and CWPs (from 6.2% at b-T0h to 29.56% at b-T24h), alongside a decrease in ribosomal proteins (from 23.5% at b-T0h to 11.9% at b-T24h), over the course of batch culture growth. This trend highlights the metabolic shift from a respiro-fermentative growth phase, characterized by a high protein synthesis activity, to a respiratory state during the diauxic phase, where the mitochondrial activity is predominant.

1.

Identification and quantification of proteins in S. cerevisiae S288c yeast strain in batch bioreactor culture in a rich medium. (A). Boxplots showing the average numbers of quantified proteins per time point during the batch culture in bioreactor. Solid lines inside the boxplots represent the average number of quantified proteins in three biological replicates, with the whiskers representing the minimum and the maximum values for each condition. (B). Venn diagram comparing the lists of proteins quantified in different time points during the batch culture in a bioreactor (left panel), with the number of quantified proteins per time point displayed in the upper right panel, and the number of time-point specific elements and shared elements between multiple time points represented in the lower right panel. The elements included in the list for each time point correspond to proteins that were identified and quantified at least in two out of three replicates. (C) Pie charts representing the annotation of cellular localization of Top 100 proteins expressed at each time point of the batch culture using Gene Ontology (GO) terms: Mitochondrial proteins (green), Cell Wall Proteins CWPs (blue), Ribosomal proteins (yellow) and other proteins (white).

Notably, the CWPs among the top 100 most abundant proteins include several moonlighting proteins such as Tdh3p, Tdh2p, Tdh1p, Ssa1p, and Ssa2p. These proteins are known to be localized to multiple cellular compartments and play roles not only in cell wall structure but also in glycolysis and stress response. Therefore, we conducted a more in-depth functional analysis of our data set to better understand the overall dynamics of protein expression in the YCW-enriched fractionand specifically the behavior of cCWPsthroughout batch fermentation in bioreactors, as detailed subsequently.

Proteomic Changes Driven by Nutrient Availability in Batch Bioreactor Culture

To comprehensively capture protein expression dynamics in the YCW-extracted fraction throughout the bioreactor batch culture, we conducted both pairwise and multivariate comparisons of samples collected at different time points.

Multivariate analysis of the data set revealed clear clustering of biological replicates for each time point, highlighting the high reproducibility of the data (Figure A). Principal component analysis (PCA) performed using Perseus software further showed that the inoculum (b-T0h) and the stationary phase (b-T24h) were distinctly separated from the early exponential (b-T4h) and early diauxic (b-T7.5h) phases. The late diauxic phase (b-T9h) clustered between the actively growing early exponential phase and the stationary phase, reflecting its transitional nature (Figure A).

2.

Differential proteomics analysis during the kinetics of S. cerevisiae S288c batch culture in a bioreactor. (A). Dimensionality reduction of proteomics data via Principal Component Analysis (PCA) using Perseus v.2.1.5.0. Software. Each filled circle represents a biological replicate, with colors indicating different time points of the culture. Principal Component 1 (PC1) accounts for 40.6% of the total variance, while Principal Component 2 (PC2) explains 22.6%. (B). Protein loadings plot showing the contribution of individual proteins to the variance captured by the principal components. Proteins with loadings < −0.05 are highlighted in blue, and those with loadings > 0.05 in red. (C). Functional enrichment analysis of proteins contributing mostly to PCA analysis (loadings highlighted in panel B), performed using STRING-DB tool. Annotated keywords were retrieved from UniProt, with a confidence score threshold of 0.8. Circle size represents the number of enriched proteins per functional category, and color indicates the value of the false discovery rate (FDR).

Principal component 1 (PC1), which accounts for 40.6% of the total variance, primarily separated the flask-grown inoculum (b-T0h) from the bioreactor samples (b-T4h to b-T24h). Principal component 2 (PC2), explaining 22.6% of the variance, distinguished the stationary phase, characterized by growth on nonfermentable carbon sources, from the active growth phases on glucose, including the early exponential and early diauxic stages (Figure A). We analyzed the differentiating loads (|score| > 0.05) retrieved from Perseus software, highlighted in blue and red in Figure B. The functional analysis of these loads using String DB (Annotated Keywords Uniprot) showed the enrichment of terms related to cell wall and glycoprotein for the loads of component 1 whose score > 0.05 and are more enriched in flask batch culture, while oxidoreductase, tricarboxylic acid cycle, gluconeogenesis, and ribosomal proteins are enriched for the loads of component 1 whose score < - 0.05 that characterize the bioreactor batch culture (Figure C and Supporting Information Figure S2A,C). For the loads of component 2, those with a score of > 0.05 were enriched in mitochondria, stress response, and oxidoreductase and are more enriched in the stationary phase time point, while those with a score of < −0.05 were enriched in ribonucleotides and ribosomal proteins in the early growth phases (Figure C and Supporting Information Figure S2B,D).

For pairwise comparisons, differentially expressed proteins between time points were identified using Student’s t-tests and visualized via Volcano plots. The results are summarized in Table .

1. Number of Differentially Expressed Proteins for Pairwise Comparison According to the Growth Phase in Batch Culture .

^aThe proteins were considered significantly differential for a false discovery rate FDR < 0.05 and a S0 value of 0.1, using Student’s t-test. Each cell of the matrix is subdivided into two, figuring two numbers with different cell colors, and indicates a comparison between two time points: one on the matrix row and the other on the matrix column. The numbers written in black in white cells denote the proteins that are overexpressed in time points on the matrix row, whereas those written in black in gray cells correspond to the proteins overexpressed in the time points on the matrix column for each comparison. Black cells correspond to the comparison between the same time points. The cells were filled on one side of the matrix for the sake of simplicity and clarity.

The inoculum sample (b-T0h) exhibited a substantial number of overexpressed proteins compared to later time points, ranging from 452 (relative to the early exponential phase, b-T4h) to 628 (relative to the stationary phase, b-T24h) proteins. Functional analysis of the 571 proteins consistently overexpressed in b-T0h, using String-DB, revealed enrichment in 12 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including those involved in lipid, N-glycan, and O-glycan biosynthesis, processes associated with cell wall integrity (Supporting Information Figure S3A).

Conversely, the number of proteins downregulated at later time points compared to b-T0h ranged from 381 (relative to the early diauxic phase, b-T7.5h) to 587 (relative to the stationary phase, b-T24h). Functional analysis of the commonly downregulated proteins indicated enrichment in 49 KEGG pathways, primarily related to metabolic processes (Supporting Information Figure S4A).

No significant differences in protein expression were observed between the early exponential phase (b-T4h) and the early diauxic shift (b-T7.5h), in contrast to the late diauxic shift (b-T9h), which exhibited a greater number of differentially expressed proteins (Table ). This likely reflects a temporal lag between transcriptional reprogramming and its translational impact during the diauxic shift, a phase characterized by molecular transitions driven by nutrient depletion between the early exponential and stationary phases. Functional enrichment analysis of commonly overexpressed proteins revealed 17 and 11 enriched KEGG pathways for the early exponential and early diauxic phases, respectively (Supporting Information Figure S3B,C), including pathways related to ribosomes and amino acid biosynthesis. Meanwhile, the analysis of commonly downregulated proteins indicated enrichment in 20 and 14 KEGG pathways (Supporting Information Figure S4B,C), involving metabolism, oxidative phosphorylation, and peroxisome functions. These findings suggest a period of high translational activity coupled to reduced respiratory activity at this stage of the culture.

Consistent with previous findings, only minimal changes were detected between the early and late diauxic phases, with just 3 proteins overexpressed in b-T7.5h and 30 in b-T9h (Table ). The functional analysis demonstrated the enrichment of 29 KEGG pathways for the commonly overexpressed proteins (Supporting Information Figure S3D) and the 3 KEGG pathways for the commonly downregulated proteins in the late diauxic shift (b-T9h) (Supporting Information Figure S4D), all related to metabolic reprogramming.

As presented in Table , the stationary phase (b-T24h) showed a significant number of differentially expressed proteins compared with all other time points. The smallest differences were observed in comparison to the late diauxic shift (b-T9h), with 249 proteins upregulated and 293 downregulated in b-T24h. In contrast, the largest differences were seen relative to the initial time point (b-T0h), with 587 proteins upregulated and 628 downregulated in b-T24h. Functional enrichment analysis of proteins commonly overexpressed in b-T24h revealed 38 KEGG pathways, primarily associated with metabolic processes such as oxidative phosphorylation and gluconeogenesis (Supporting Information Figure S3E). Meanwhile, the commonly downregulated proteins in b-T24h were enriched in 16 KEGG pathways, including those related to ribosomal function, RNA metabolism, and cell wall integrity (Supporting Information Figure S4E).

Then, we compared the upregulated proteins for a specific time point compared to others as well as the downregulated proteins (Supporting Information Table S2). The lists of common proteins were analyzed using YEASTRACT+ , to find transcription factors (TFs) that regulate their expression (Supporting Information Table S2). Most TFs were shared between the sets of upregulated and downregulated proteins, indicating a largely overlapping regulatory network. Only a small subset appeared to be time point-specific, with 13 TFs associated with the upregulation of 154 proteins and 6 TFs linked to the downregulation of 91 proteins (Supporting Information Table S2). Functional enrichment analysis revealed that proteins specifically upregulated during the culture were mainly associated with the cell periphery and mitochondrial functions, suggesting increased cell wall activity and metabolic adaptation (Supporting Information Figure S5A). In contrast, downregulated proteins were enriched in nucleosome-associated components, consistent with reduced chromatin-related processes during later stages of growth (Supporting Information Figure S5B). A subset of 35 proteins was found to be regulated by these specific TFs and was enriched in metabolic pathways (Supporting Information Figure S5C), highlighting the importance of metabolic remodeling throughout the culture. Notably, two uncharacterized TFs, YKL222C and YPR196W, were predicted to regulate the expression of 30 of these proteins, suggesting a potential role in controlling metabolic and cell surface–associated processes.

Time-Dependent Dynamic Expression of Cell Wall Proteins in Batch Bioreactor Culture

The functional enrichment of terms related to the cell wall, such as cell periphery, glycoproteins, and glycans biosynthesis, led us to examine in great detail the temporal expression profile of these proteins, in the form of heatmaps. The heatmaps were constructed using Morpheus (https://software.broadinstitute.org/morpheus/), based on z-score normalization to average and standard deviation (data from Supporting Information Table S3).

Hierarchical clustering using the Euclidean distance was performed based on CWP abundances. In total, 44 CWPs were quantified in our data set, including 17 proteins that are also known to localize to other organelles (shown in blue in Supporting Information Table S3 and in regular font in Figure ). As illustrated in Figure , replicates from each sampling time point clustered tightly together, except for one replicate of b-T7.5h and another one of b-T9h, both corresponding to the metabolic transition phase of the culture. The b-T0h samples formed a distinct cluster that was clearly separated from all other time points (Figure ).

3.

Hierarchical clustering heatmap by Euclidean distance for z-score normalized protein abundances established with Morpheus software, showing time point-specific expression signatures of CWPs in S. cerevisiae grown in batch mode with a rich medium in a bioreactor. Our data set corresponds to label-free bottom-up quantitation applied to the S288c reference strain. Proteins exhibiting statistically significant changes following an ANOVA test are indicated in bold . The scale for z-score ranged between −2 (dark blue) and 2 (dark red), centered around 0 (white). Protein names in regular font correspond to CWPs that can be located on other organelles, whereas those written in bold italic represent the proteins that are strictly linked to the YCW.

At b-T0h, 27 CWPs were highly expressed (in red), while 10 were downregulated (in blue) compared to the subsequent time points. Notably, the expression of these 10 downregulated proteins increased as batch cultivation progressed in the bioreactor. Most of these proteins (Tdh3p, Tdh1p, Tdh2p, Cdc12p, Asp1p, Cdc3p, Cdc11p, Ssa1p, and Ssa2p) are associated with multiple organelles, except for Bgl2p, which is covalently linked to the YCW (Figure ).

Row clustering revealed two major protein groups. The first cluster contains 7 proteins (Hsc82p, Hsp82p, Tdh1p, Tdh2p, and Tdh3p), all of which may localize outside the cell wall. Within this cluster, Hsp82p and Hsc82p displayed high expression levels at b-T0h that steadily decreased throughout the culture, reaching their lowest abundances at the end of fermentation. In contrast, the remaining proteins in this cluster showed the opposite trend: minimal expression at b-T0h followed by a progressive increase, peaking at b-T24h. The second cluster is subdivided into 3 subclusters: the first subcluster included 4 proteins (Fba1p, Ape2p, Cdc12p, and Bgl2p), whose expression varied across the batch culture and mainly increased during the transition phase (b-T7.5h and b-T9h). Fba1p and Bgl2p expression peaked at b-T24h, unlike Cdc12p, whereas Ape2p expression peaked at b-T0h. The second subcluster contained 14 proteins (Ccw14p, Scw4p, Cwp1p, Gas1p, Pir1p, Tos1p, Ecm33p, Utr2p, Gas3p, Sag1p, Crh1p, Gas4p, Hsp150p, and Cis3p), 13 of which are covalently linked to the YCW (shown in bold and italic in Figure ). These proteins were strongly upregulated at b-T0h and are primarily involved in the YCW structure and remodeling. The last subcluster exhibited 19 CWPs with more complex expression profiles. Of these, 10 were upregulated in b-T0h (Uth1p, Sim1p, Jsn1p, Fus3p, Scw11p, Dse4p, Rax1p, Pho3p, Scw10p, and Pho11p), with five covalently linked CWPs (highlighted in bold and italic in Figure ). Six proteins were specifically upregulated at the stationary phase (b-T24h) during the batch culture in the bioreactor (Figure ). These proteins are Fba1p, Fbp1p, Pst2p, Suc2p, Pho5p, and Exg1p, mostly implicated in carbon metabolism when fermentable carbon sources are limited.

During the transition diauxic phase, the expression of some proteins is increased compared to the other time points of the batch culture, including Uth1p, Hsp150p, Cwp1p, Gas1p, and Sim1p (Figure ). During the exponential growth, the majority of CWPs were downregulated (Figure ). Spearman rank correlation of CWPs expression showed 29 CWPs whose expression was positively correlated (correlation coefficient > 0.5, red bars in Supporting Information Figure S6), mostly containing proteins that are strictly located to the YCW, whereas 12 CWPs were negatively correlated (correlation coefficient < −0.5, blue bars in Supporting Information Figure S6), mostly containing proteins that can be in other organelles.

To provide a clearer visual overview of each CWP expression over time, we generated kinetic plots by averaging normalized abundances for each CWP of the biological replicates at each time point and expressing them relatively to the total abundance of the CWP across all time points. The corresponding standard deviations are also displayed. We also differentiated canonical cCWPs found exclusively in the YCW (Figure ) from those that can be present in other organelles (Figure ).

4.

Temporal expression dynamics of canonical cCWPs during batch cultivation of the wild-type S. cerevisiae S288c strain in a bioreactor. Values represent the mean normalized abundance for each cCWP across three biological replicates at a specific time point, relative to the total abundance of each cCWP across all time points. Error bars indicate the standard deviation. Underlining indicates CWPs whose expression significantly varies during the batch culture kinetics following ANOVA statistical analysis on log₂ – normalized abundance (p-value < 0.05).

5.

Temporal expression dynamics of CWPs that are also localized in other organelles in batch cultivation of the wild-type S. cerevisiae S288c strain in a bioreactor. Values represent the mean normalized abundance for each CWP across three biological replicates at a specific time point, relative to the total abundance of each CWP across all time points. Error bars indicate the standard deviation. Underlining indicates CWPs whose expression significantly varies during the batch culture kinetics following ANOVA statistical analysis (p-value < 0.05) on log₂ – normalized abundance.

As shown in Figure , the kinetic expression profiles of several cCWPs change over time during batch cultivation. Most CWPs display their highest levels at b-T0h and reach their lowest expression at b-T4h. This aligns with the fact that these proteins are needed for transition stress at b-T0h and their expression is downregulated when the cell wall is flexible during the early exponential phase. Among these proteins, some that are linked to stress adaptation as well as cell wall remodeling and reinforcement show a slight increase in their expression over time during the diauxic shift and then decrease at the end of fermentation (Cis3p, Crh1p, Cwp1p, Ecm33p, Gas1p, Hsp150p, Sag1p, Scw4p, Utr2p), while others display a slight increase at the end of fermentation as Ccw14p, Pho11p, and Pho12p, consistent with their involvement in nutrient signaling during depletion at the end of the culture. The remaining proteins show a relatively flat kinetic profile throughout the culture (Gas3p, Gas5p, Pho3p, Pir1p, and Scw10p), consistent with their role as YCW maintenance proteins that do not depend strongly on nutrient or stress pathways. Scw11p displayed a progressive decrease in abundance throughout fermentation, reaching its lowest level at the late diauxic shift. This decline corresponds to the reduced need for cell wall remodeling as cell division slows and cells transition toward a quiescent, nutrient-limited state. Toward the end of fermentation, Scw11p levels showed a modest increase, suggesting a limited resumption of cell wall turnover associated with stationary-phase maintenance and long-term stress adaptation. Sim1p expression shows a complex, multiphasic expression pattern, peaking when yeast cells undergo major physiological transitions, such as at the initiation and the early diauxic shift. This suggests a role in cell wall remodeling and adaptation to metabolic stress. Its decline during exponential growth and toward the end of fermentation reflects reduced need for such remodeling when cells are either rapidly proliferating or entering quiescence. Dse4p maintained low, relatively constant abundance throughout the batch culture, reflecting its role as a basal cell wall protein with a specialized, nondominant function under these conditions.

Bgl2p, Ssa1p, and Ssa2p follow similar trends: their expression is minimal at b-T0h, increases during the batch process, and then slightly varies for the rest of the culture. This expression pattern might be explained by the low activity of quiescent cells at b-T0h, where cells are not experiencing a strong growth-related cell wall expansion. Upregulation during the active division phase of batch culture supports increased glucan remodeling and protein folding. This activity stabilizes once the culture reaches a dynamic physiological equilibrium in the stationary phase.

In contrast, Exg1p, Pst2p, Pho5p, and Suc2p exhibit fluctuations over time, reaching their maximum expression at the end of fermentation (b-T24h). The late-culture upregulation suggests that these proteins play a protective and adaptive role during nutrient depletion, helping cells to remodel and maintain the YCW under stress (Exg1p, Pst2p) and optimize phosphate acquisition (Pho5p) and carbon metabolism (Suc2p) when environmental nutrients are low, to survive prolonged culture conditions. This reflects the transition from growth-focused metabolism to a survival and stress-adaptation mode.

cCWPs that did not show significant variation across the culture according to the ANOVA test displayed relatively higher variability, as observed for Cis3p, Hsp150p, Cwp1p, Crh1p, Dse4p, Pho11p, Pho12p, Sag1p, Sim1p, and Suc2p. These proteins represent either structural cCWPs heavily O-glycosylated that are integrated into the cell wall matrix or cCWPs present at low abundance.

The same analysis was carried out for the CWPs that can be found in other organelles (Figure ). Apart from the slight variation between replicates for each of these proteins except for Uth1p, Jsn1p, Rax1p, we can clearly see that their expression is time-dependent during the batch culture. The expression of Tdh1p, Tdh2p, and Tdh3p shows the same trend as Ssa1p and Ssap2, with a minimal value at b-T0h where the cells are quiescent and is increased during the batch culture while remaining relatively constant all along, showing their implication in actively dividing cells. The expressions of Hsp82p, Hsc82p, Rax1p, and Tos1p showed the opposite trends, where their expression is maximal at b-T0h during the quiescence followed by a decrease and then stable levels during the rest of the batch culture. This pattern is consistent with early phase stress adaptation, cell cycle/morphogenesis transitions, and the shift from quiescence to exponential growth.

Asp1p, Cdc11p, and Fus3p show a peak in the exponential phase (b-T4h), followed by a later decrease during the culture. This dynamic expression profile might be related to their role in supporting active division and growth-related signaling. As conditions worsen and growth slows, their expression naturally decreases as part of the transition toward stationary phase physiology. They are also of low abundance compared to the other CWPs that can be found in other organelles. The other septins Cdc12p and Cdc3p expression profile show a peak at the early diauxic phase (b-T7.5h) and the late diauxic phase (b-T9h), respectively. Indeed, the diauxic shift requires reorganization and reinforcement of septin structures, with Cdc12p upregulated during early cytoskeletal remodeling and Cdc3p rising later to support long-term septin stability as cells approach the stationary phase. Nevertheless, Uth1p, a member of the SUN family, showed a highly dynamic expression during the batch culture, peaking at the initiation (b-T0h) and the early diauxic phase (b-T7.5h), likely reflecting its regulation by multiple stress and metabolic pathways. Its roles in mitochondrial function, oxidative stress response, and cell wall remodeling make its abundance particularly sensitive to the changing physiological conditions encountered throughout fermentation.

Jsn1p and Ape2p displayed a similar expression profile across the culture with a maximum at b-T0h and a slight decrease during the early diauxic shift. This mild reduction likely reflects the transient global downregulation of translation and metabolic reorganization that occurs when cells transition from fermentation to respiration. Because both proteins function in general growth and metabolic maintenance rather than carbon source – regulated pathways, their abundance remains largely constant throughout the batch culture.

Fba1p and Fbp1p showed slightly variable expression in the early stages of batch culture, reflecting the metabolic transition from stationary-phase carryover to active fermentation. Their abundances increased markedly toward the end of the culture, reaching a maximum at b-T24h, consistent with glucose depletion and the induction of gluconeogenesis and respiratory metabolism. The strong late-stage increase in Fbp1p corresponds to its role as a glucose-repressed, starvation-induced enzyme, while Fba1p remains required for both glycolytic and gluconeogenic fluxes under nutrient-limited conditions. Consistent with previous reports of their cell wall localization, , the late-stage accumulation of Fba1p and Fbp1p suggests a functional link between metabolic reprogramming and the structural maintenance during the stationary phase. Their presence at the cell periphery is likely to ensure that gluconeogenic flux is channeled directly into the synthesis of β-glucan and chitin synthesis needed for the wall strengthening and integrity maintenance under nutrient limited conditions and environmental stresses of the stationary phase.

Benchmarking Cell Wall Proteins Dynamic Profiling in Batch Culture via Targeted Reanalysis of Whole-Cell Lysate Data Sets

To assess the robustness and biological coherence of the temporal patterns observed in our data set, we compared our results with two published proteomic studies that examined protein dynamics in S. cerevisiae cultured in batch mode. , While these proteomic studies provided a comprehensive map of the total yeast proteome during batch culture, the specific dynamics of the cell wall remained secondary in such global analyses. Here, we reinterrogated these whole lysate data sets. By specifically filtering for proteins with known cell wall localization or GPI anchors, we provide a focused view of how the yeast surface undergoes remodeling, despite the lack of physical CWP enrichment in the original samples. Both studies used TMT-based quantitative proteomics and sampled multiple growth phases, providing an appropriate benchmark for evaluating the CWP expression trends.

Den Ridder et al. profiled two strains, CEN.PK113–7D (wild type) and an engineered minimal glycolysis mutant (MG), grown under aerobic and anaerobic conditions in controlled bioreactors. Using a TMT 10-plex workflow, they quantified 36 CWPs, fewer than those detected in our study (Supporting Information Figure S7 and Supporting Information Table S4). Their data set revealed three major classes of CWP behavior (Supporting Information Figure S8): (i) proteins whose abundance was primarily condition-dependent depending on oxygen availability (e.g., Scw4p, Gas3p, Ssa1p, Bgl2p, Pst2p, Suc2p and Exg1p); (ii) proteins with strain-specific expression (e.g., Hsp82p, Tdh3p); and (iii) proteins influenced by both strain and growth condition, including several strictly cell wall – localized proteins such as Gas1p, Ssa2p, Cis3p, Utr2p, Ygp1p, Pst1p, Gas5p, Ecm33p. A subset of CWPs was detected exclusively in particular strain - condition combinations: for example, Yps1p, Sim1p, Pho3p, Pho5p, Pho11p, and Pho12p appeared only in the MG strain under anaerobic conditions. Many CWPs showed little temporal variation across growth phases, while correlation analyses identified predominantly positive associations among strictly cell wall - localized proteins (Supporting Information Figure S9). Overall, the study demonstrated condition- and strain-dependent regulation of CWP abundance, consistent with the dynamic shifts observed in our data set.

Murphy et al. investigated proteomic remodeling during the diauxic shift in two wild-type strains (BY4742 and DBY7286) and a Hap2p-deficient mutant cultured aerobically in YPD in flasks. Their TMT 10-plex coupled to 2D-LC MS³ workflow quantified 56 CWPs in BY4742 and 51 CWPs in DBY7286, including several proteins not detected in our data set (Supporting Information Figure S7 and Supporting Information Table S5). In BY4742, CWPs segregated into Hap2-dependent and Hap2-independent groups (Supporting Information Figure S10A,B). Hap2-dependent proteins exhibited either continuous accumulation throughout culture (Ccw14p, Pst2p, Ssa1p, and Ygp1p) or a midculture peak followed by a decline (Cis3p, Cts1p, Cwp1p, Dse4p, Ecm33p, Exg1p, Pho5p, Pho11p, Pry3p, Pst1p, and Scw11p), while certain proteins (e.g., Pho3p) remained stable. Hap2p deletion strongly upregulated proteins such as Plb1p and Tdh1p (Supporting Information Figure S11). In the DBY7286 strain, strictly cell wall - localized CWPs displayed 4 major temporal profiles, including midculture peaks (as for: Aga2p, Bar1p, Cis3p, Ecm33p, Exg2p, Gas1p, Gas3p, Scw4p, Scw10p, Scw11p, Sim1p, Pho3p, Pho11p), gradual increase (as for: Ath1p, Gas5p, Pst1p, Pst2p, Ssa1p, Ygp1p), or progressive decrease (as for: Bgl2p, Cts2p, YJL171Cp, Ssa2p, Suc2p), or variable complex trends (as for: Cwp1p, Exg1p, and Ccw14p) (Supporting Information Figure S12). CWPs with multiorganelle localization exhibited 6 additional temporal patterns, reflecting the metabolic shift associated with glucose depletion and the onset of respiration (Supporting Information Figure S13).

Comparison of CWP sequence coverage across the studies showed that our data set generally yielded higher values, particularly for covalently linked CWPs and for TDH proteins (Supporting Information Figure S14 and Supporting Information Table S6). This likely reflects the sample preparation strategy used in our study that enriched the YCW fraction. In contrast, CWPs that may be localized on other organelles exhibited higher sequence coverage in the study by Murphy et al., likely due to the extensive fractionation and separation methods applied in their workflow (Supporting Information Figure S14 and Supporting Information Table S6).

Across both external data sets, CWPs consistently showed dynamic phase-dependent expression, paralleling and complementing the patterns observed in our study. Moreover, a genotype-specific expression was also shown in their studies. While differences in strain backgrounds, culture conditions (bioreactor vs flask), and analytical workflows likely contributed to variation in detected proteins and temporal profiles, the overarching agreement across studies supports the biological robustness of our findings. Together, these comparisons reinforce the conclusion that CWP expression dynamic remodeling is a conserved and reproducible feature of S. cerevisiae growth in batch culture.

Discussion and Conclusion

The quantification of approximately 1500 proteins in this study provides a comprehensive map of the YCW fractions across distinct growth phases. Although the detection of noncell wall proteins, likely due to organelle contamination or “moonlighting” functions, indicates that YCW enrichment could be further optimized, our protocol remains highly effective. The superior abundance and sequence coverage of classical canonical cell wall proteins (cCWPs) observed in our study demonstrate a clear advantage over methods utilizing total cell lysates. , Despite the fact that cell wall remodeling during batch fermentation may influence extraction efficiency, all samples were processed using a standardized protocol, and the reproducibility across biological replicates supports the robustness of the observed trends.

Our differential proteomics analysis reveals a tight coordination between carbon source availability and cell wall composition in proteins. The dynamic distribution of TFs serves as the regulatory bridge between yeast metabolic states and cell wall composition in proteins. During the exponential phase, the proteome is dominated by protein synthesis and cellular growth machineries, orchestrated by transcription and proliferation related TFs. At this stage, cells maintain a flexible cell wall optimized for rapid expansion and multiplication, characterized by the expression of enzymes such as Bgl2p and Scw11p, which are essential for glucan assembly and restructuring. The diauxic shift represents the most profound proteomic rearrangement observed in S. cerevisiae. As glucose is depleted, the transition from respiro-fermentative to fully respiratory metabolism induces a major transcriptional reprogramming. This shift, governed by specific regulators, increases proteins involved in respiration and energy production while decreasing those linked to protein synthesis. Correspondingly, the cell wall begins to strengthen in preparation for nutrient limitation, marked by the incorporation of stress-response proteins such as Hsp150p, Cis3p, and Cwp1p, alongside remodeling enzymes like Gas1p and Utr2p. These outcomes support and extend findings from previous investigations. ,,−

Upon depletion of nutrients in batch culture, yeast cells transition into quiescence, a vital survival strategy characterized by metabolic and structural shifts. Our results show a significant enrichment of proteins associated with the cell periphery and plasma membrane in quiescent cells, reflecting a remodeling process that bolsters structural integrity and longevity. , The induction of Pir proteins and increased cross-linking of glucans and chitin provide the necessary rigidity to withstand the environmental stressors. The induction of these proteins seems to be associated with glucose starvation and oxidative stress. The involvement of uncharacterized factors such as YKL222C and YPR196W suggests specific regulatory nodes that fine-tune these architectural adaptations in response to nutrient depletion and stress. While both the seed culture (b-T0h) and the late-stage bioreactor sample (b-T24h) represent stationary phases marked by growth arrest, their proteomic profiles differ substantially, a distinction that has not been previously documented. The b-T0h profile reflects a high-stress state, characterized by the upregulation of Hsp82p and structural proteins, such as Ccw14p and Pir1p. This is likely a consequence of the uncontrolled pH and near anaerobic conditions inherent to flask-based cultures. Conversely, b-T24h represents a more stable, regulated quiescent state, as evidenced by the upregulation of gluconeogenic enzymes like Fba1p and Fbp1p. Furthermore, the sustained expression of Tdhp isoforms and Ssa1p/2p molecular chaperones throughout the culture suggests a coordinated metabolic strategy. By maintaining active protein quality control and baseline metabolic activity, the cells remain “primed” for rapid adaptation or regrowth upon the restoration of favorable conditions. Besides their potential moonlighting functions, these chaperones and glycolytic proteins may appear in cell wall preparations because of their prevalence in the total proteome, which increases the likelihood of their detection during enrichment.

Benchmarking our results against Murphy and den Ridder studies highlights how variations in sample preparation, strain background, and culture conditions shape the detectable proteome. While certain starvation proteins like Ygp1p were absent, our workflow uniquely identified key structural and adhesion proteins (Sag1p and Pir1p), demonstrating high sensitivity. These discrepancies underscore the necessity of integrating multiple data sets for a comprehensive view of CWPs expression. Moving forward, we will implement refined purification strategies to enhance the detection of low-abundance CWPs and explore the influence of pH and temperature. Our time-course analysis reveals that cCWP expression shifts align with metabolic transitions across growth phases, offering valuable insights for both fundamental biology and industrial applications.

Glossary

Abbreviations

ANOVA

analysis of variance

CWP

cell wall protein

cCWP

covalently bound Cell Wall Protein

EDTA

2,2′,2″,2‴-(Ethane-1,2-diyldinitrilo)­tetraacetic acid

eFASP

enhanced filter-assisted sample preparation

FDR

false discovery rate

GO

gene ontology

GPI

glycosylphosphatidylinositol

H2SO4

sulfuric acid

HPLC

high performance liquid chromatography

KEGG

Kyoto Encyclopedia of Genes and Genomes

(2D-)­LC

(bidimensional) liquid chromatography

MG

minimal glycolysis

NaCl

sodium chloride

NaOH

sodium hydroxide

OD

optical density

PCA

principal component analysis

SDS

sodium dodecyl sulfate

SGD

Saccharomyces Genome Database

TF

Transcription Factor

TMT

Tandem Mass Tag

Tris–HCl

2-Amino-2-(hydroxymethyl)­propane-1,3-diol, chlorhydrate

YCW

Yeast Cell Wall

YPD

Yeast Extract – Peptone – Dextrose.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.6c02870.

• Proteomics data sets, including both raw and processed result files for all yeast cell wall (YCW) isolates treated using different sample preparation methods, obtained from the three independent biological replicates, are freely accessible in the PRIDE repository (PXD071725); additional information about the growth in batch culture mode in bioreactors, as well as visualization of CWP expression patterns, temporal dynamics, and comparative analyses of CWP expression and sequence coverage across strains and conditions in other studies; and detailed quantitative proteomics data, lists of differentially expressed proteins, functional annotations, and sequence coverage information. Pearson correlation coefficients of proteomic profiles across biological replicates and growth phases during batch fermentations in bioreactor of S. cerevisiae S288c yeast (XLSX)

• Pairwise differential analysis of proteins expression and regulation during the batch culture in bioreactor of S. cerevisiae S288c (XLSX)

• Abundances of proteins from the batch culture in bioreactor of S. cerevisiae S288c (XLSX)

• Abundances of proteins from the batch culture in bioreactor of S. cerevisiae CEN.PK133-7D wild type and mutant MG, under both aerobic (A) and anaerobic (AA) conditions, retrieved from den Ridder et al. study (XLSX)

• Relative abundances of proteins from the batch culture in bioreactor of S. cerevisiae DBY7286 strain, BY4247 strain and its mutant with Hap2 deletion, retrieved from de Murphy et al. study (XLSX)

• Comparison of CWP sequence coverage (%) between our dataset and the values reported by Murphy et al. and den Ridder et al. (XLSX)

• Supplementary information document, containing all the supplementary figures (SI Figure 1 - SI Figure 14) (PDF)

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(M.Y.) Current affiliation: Institut Pasteur, Université Paris Cité, CNRS UAR2024, 26–28 Rue du Docteur Roux, F-75724 Paris, France

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

The authors declare no competing financial interest.