1 Introduction
Frozen dough technology is widely used in industrial baking because it separates dough preparation from final baking, allowing centralized production, flexible scheduling, extended distribution, and more stable supply of baked products [
1]. A major biological constraint in this process remains the reduced performance of baker’s yeast during frozen storage and after thawing. Yeast cells must survive freezing, recover rapidly during thawing and proofing, and resume carbon metabolism fast enough to generate sufficient CO
2 for dough expansion. When these processes are delayed or incomplete, frozen dough often produces breads with reduced loaf volume, compact crumb structure, and poor texture [
2]. The problem is not limited to one freezing event. In practical distribution chains, dough can experience repeated or partial freeze–thaw fluctuations during storage and transportation, which increases cumulative injury to yeast cells and aggravates product variability [
3]. At the cellular level, freeze–thaw stress combines ice-associated mechanical stress, extracellular dehydration, osmotic imbalance during rehydration, membrane phase perturbation, protein damage, and oxidative stress during thawing [
4,
5]. Although
S. cerevisiae has endogenous protective mechanisms such as compatible solute accumulation, antioxidant enzymes, membrane remodeling, and stress-induced protein quality control, these responses are often insufficient in frozen dough environments where survival must be coupled to rapid fermentation recovery [
6,
7].
Current strategies for improving yeast freeze–thaw tolerance include screening naturally tolerant strains, modifying individual stress-related genes, and adding cryoprotective ingredients to dough or culture systems [
8,
9]. These approaches have been valuable, but they also show clear limitations. Single-gene or additive-based interventions may improve viability under a defined assay condition without ensuring robust recovery across strain backgrounds, dough formulations, or storage regimes. In addition, some tolerance strategies introduce trade-offs in growth, fermentation rate, or product quality, which is a serious concern for industrial yeast use [
10,
11]. More importantly, freeze–thaw performance in frozen dough is not a simple survival trait. A useful strain must maintain membrane and cell-wall integrity, suppress excessive oxidative damage, preserve metabolite pools needed for recovery, and restart sugar utilization and fermentation after thawing. This complexity makes freeze–thaw tolerance a suitable target for adaptive laboratory evolution (ALE), which can select coordinated phenotypes over many generations without imposing a predefined molecular solution [
12,
13]. ALE has been applied to improve yeast tolerance to ethanol, osmotic stress, acids, and other industrial stresses [
14–
16], yet long-term freeze–thaw evolution explicitly connected to frozen dough functionality and multi-omics interpretation remains comparatively limited.
The expanding use of omics technologies in agricultural and food research makes it possible to connect process-level performance with cellular regulation and biochemical state [
17,
18]. This is particularly relevant for freeze–thaw adaptation because the phenotype is distributed across several biological layers rather than controlled by a single pathway [
19]. Transcriptomics can reveal whether an evolved strain redirects transcriptional resources toward transport, stress response, sulfur metabolism, cell-cycle-related recovery programs, or cell-envelope functions [
20]. Metabolomics can identify whether such regulation is accompanied by changes in amino acid pools, glutathione-related compounds, carbon intermediates, and lipid-associated metabolites [
21]. Integrated analysis then allows these two data layers to be interpreted as coordinated modules rather than independent lists of genes and metabolites. In frozen dough, this layered interpretation is useful because food performance depends on how cellular adaptation is translated into fermentation recovery, gas production, and final product structure [
1].
Here, three independent S. cerevisiae lineages derived from the laboratory-related strain DBY12007 and two wild isolates, PGY7 and PGY34, were evolved for 300 generations under repeated freeze–thaw selection. The evolved populations were evaluated for viable cell yield, growth fitness, freeze–thaw survival, and spot recovery, followed by frozen dough baking tests to verify functional relevance. Physiological assays then examined post-thaw growth recovery, fermentation-associated parameters, reactive oxygen species (ROS) accumulation, antioxidant capacity, and cell-envelope morphology. Because the DBY12007-derived lineage showed the clearest survival and recovery phenotype and has an S288C-related genetic background, ALE1 and its ancestor were selected for transcriptomic, metabolomic, and integrated omics analyses. This workflow was designed to build a continuous evidence chain from selection and phenotype to food function, physiological mechanism, and omics-based interpretation.
2 Materials and Methods
2.1 Yeast strains and culture conditions
The ancestral
Saccharomyces cerevisiae strains used in this study were DBY12007, PGY7, and PGY34. DBY12007 is a wild-type diploid derivative of the laboratory reference background S288C, whereas PGY7 and PGY34 are wild diploid isolates from wine fermentation and oak exudate environments, respectively [
7,
22,
23]. For clarity, DBY12007, PGY7, and PGY34 are referred to as Ancestor1, Ancestor2, and Ancestor3, respectively, when compared with their corresponding evolved lineages. Strains were stored in 25% glycerol at −80 °C and streaked onto yeast extract-peptone-dextrose (YPD) agar plates before experimentation. Unless otherwise specified, yeast cultures were grown in YPD medium containing 1% yeast extract, 2% peptone, and 2% glucose (Sangon Biotech, Shanghai, China). For solid medium, 2% agar (Solarbio, Beijing, China) was added. Liquid cultures were incubated at 30 °C with shaking at 200 r/min using a New Brunswick Innova 44 shaker (Eppendorf, Hamburg, Germany). All inocula were prepared from overnight YPD cultures to maintain a consistent physiological state across assays.
Throughout this study, ALE1, ALE2, and ALE3 denote independently evolved lineages derived from DBY12007, PGY7, and PGY34, respectively. Unless otherwise stated, these terms refer to archived mixed evolved populations collected at the indicated generation checkpoints, rather than randomly selected single-colony isolates. The generation-300 evolved populations were used as endpoint materials for freeze–thaw survival, growth recovery, frozen-dough baking, antioxidant-capacity assays, and scanning electron microscopy. For transcriptomic, metabolomic, and integrated omics analyses, the generation-300 ALE1 population and its corresponding ancestor DBY12007 were used because ALE1 showed the strongest survival and recovery phenotype and has an S288C-related background. Single colonies obtained during routine streaking were used only for culture-purity monitoring and archiving of representative colonies; they were not used to impose clonal bottlenecks or to define the endpoint ALE materials.
2.2 Adaptive Laboratory Evolution
ALE under freeze–thaw stress was performed using three independent Saccharomyces cerevisiae lineages derived from DBY12007, PGY7, and PGY34. Each ALE generation consisted of one complete freeze–thaw selection cycle, including growth, freeze–thaw exposure, recovery of surviving cells, and serial transfer. Briefly, stationary-phase cultures grown in YPD medium at 30 °C and 200 r/min for 24 h were harvested by centrifugation (6000 r/min, 5 min), and cell pellets were resuspended in 1 mL of the designated freeze–thaw medium. Cells were frozen at −80 °C for 18 h and thawed at 30 °C for 10 min, followed by recovery in fresh YPD medium (30 °C, 200 r/min, 24 h). After recovery, 100 μL of culture was transferred into 10 mL fresh YPD medium (1:100, v/v) to initiate the next ALE generation.
The evolution experiment was conducted for 300 generations, corresponding to 300 consecutive freeze–thaw selection cycles. The selection pressure was progressively modified through three phases. During generations 1–100, YPD was used as the freeze–thaw medium. During generations 101–200, sterile ddH2O was used to introduce a low-osmolarity environment. During generations 201–300, PBS buffer (pH 7.4) was used to provide a defined ionic environment during freeze–thaw stress. This stepwise adjustment increased selection pressure and promoted adaptation to repeated freezing and thawing conditions.
Evolved populations were archived and evaluated at the 0th, 100th, 200th, and 300th generations. To monitor culture purity, evolving populations were streaked onto YPD agar plates every 10 generations. Colonies obtained from these plates were used only for purity verification and archiving representative isolates; the subsequent ALE cycle was initiated from the recovered mixed population rather than isolated colonies. Therefore, the evolution process maintained population-level diversity throughout the 300-generation selection. The −80 °C treatment was used as a stringent selection pressure, whereas the −20 °C cell-based freeze–thaw assay and frozen-dough storage were used for phenotype validation and application-level evaluation.
2.3 Freeze–thaw tolerance assay
To validate freeze–thaw tolerance under a moderate repeated-freezing regime, yeast suspensions were subjected to eight consecutive freeze–thaw cycles at −20 °C. This assay was used for phenotype validation rather than for evolution. Stationary-phase cultures grown in YPD medium were harvested by centrifugation (6000 r/min, 5 min), washed once, and resuspended in sterile distilled water. An aliquot was serially diluted and plated onto YPD agar to determine the initial colony-forming units (CFU1). The remaining suspension was divided into aliquots and subjected to eight freeze–thaw cycles, with each cycle consisting of freezing at −20 °C for 18 h followed by thawing at 4 °C for 6 h. After each thawing step, samples were vortexed to ensure homogeneous resuspension before being returned to the freezer. After the final cycle, samples were serially diluted and plated onto YPD agar to determine post-treatment colony-forming units (CFU2). Cell survival was calculated as follows:
where CFU1 and CFU2 represent viable cell counts prior to and following freeze–thaw stress, respectively.
2.4 Growth kinetics and doubling time
Growth kinetics of yeast strains were monitored using a Synergy H1 multimode microplate reader (BioTek Instruments, Winooski, VT, USA) following established microplate cultivation procedures (Held, 2010). All strains were pre-cultured overnight in 4 mL YPD medium at 30 °C with shaking at 220 r/min until stationary phase. Cells were harvested by centrifugation at 6000 r/min for 5 min using an Eppendorf 5424R centrifuge (Eppendorf, Hamburg, Germany), washed twice with sterile phosphate-buffered saline (PBS; Solarbio, Beijing, China), and re-inoculated into fresh YPD medium in sterile, flat-bottom 96-well plates (Haizhixing Experimental Equipment Co., Ltd., Jiangsu, China) at an initial OD600 ≤ 0.1. During cultivation, plates were incubated at 30 °C under continuous orbital shaking (559 r/min, 1 mm amplitude), and optical density at 600 nm (OD600) was automatically recorded at regular intervals. Growth curves were generated for each strain under each tested condition. Data acquisition and processing were performed using Gen5 Microplate Reader and Imager Software (Agilent Technologies, Santa Clara, CA, USA). The doubling time (DT) was calculated from OD600 measurements obtained during the exponential growth phase using the following equation:
where OD1 and OD2 represent the initial and final optical densities, respectively, and t1 and t2 are the corresponding time points (hours).
2.5 Frozen dough preparation and bread property analysis
Frozen-dough bread was prepared using a two-stage fermentation and freezing protocol. For each formulation, a total of 150 g wheat flour was used as the flour basis. A pre-ferment was prepared by mixing 90 g high-gluten wheat flour (11% protein, 1.6% fat, 73.5% carbohydrates; Yihai Kerry Arawana Holdings Co., Ltd., Shanghai, China), 54 g water, 0.45 g maltase (Solarbio, Beijing, China), and S. cerevisiae at 1.0% (w/w flour basis). Maltase was added because preliminary tests showed limited maltose utilization by the yeast populations used in this study; therefore, it was included to provide fermentable sugars and minimize the influence of strain-dependent maltose utilization on the evaluation of freeze–thaw tolerance. The enzyme was used according to the manufacturer’s specification.
Pre-fermentation was carried out at 30 °C for 2 h. The dough was then frozen at −20 °C for 4 days, thawed at 4 °C for 12 h, and fermented again at 30 °C for 2 h. This −20 °C frozen dough treatment was used as an application-level validation rather than a selection regime. It allowed evaluation of whether ALE-derived yeast populations could maintain fermentation functionality and baking performance in a dough matrix after frozen storage. Subsequently, 60 g flour, 36 g water, 7.5 g sucrose, and 2.25 g sodium chloride were added and mixed until homogeneous. Butter (7.5 g) was incorporated, and kneading continued until the gluten reached the windowpane stage. Dough pieces were molded, proofed at 30 °C for 1 h, and baked at 170 °C for 30 min.
Three independent dough batches were prepared for each yeast population. Each batch produced three loaves, and two central slices from each loaf were used for texture analysis. After cooling, loaf specific volume was measured by the millet displacement method. Crumb texture was evaluated by Texture Profile Analysis using a TA.XT Plus Texture Analyzer (Stable Micro Systems, Surrey, UK). Loaves were cut into 15 mm slices, and two center slices per loaf were analyzed using a P/36 cylindrical probe with a trigger force of 5 g, pre-test, test, and post-test speeds of 1.0, 3.0, and 3.0 mm/s, 50% compression, and a 1 s interval.
2.6 Batch fermentation profiling and metabolite quantification
Batch fermentations were conducted in 250 mL baffled flasks containing 50 mL YPD at an initial OD600 of 0.1. Cultures were incubated at 30 °C and 200 r/min for 48 h under shake-flask conditions. Samples were collected at regular intervals from 0 to 48 h using three independent biological replicates. Cell growth was monitored by measuring OD600 after appropriate dilution, and pH was measured using a calibrated FE28 pH meter (Mettler Toledo). Culture samples were centrifuged at 12,000 × g for 10 min at 4 °C, and the supernatants were used for metabolite quantification.
Residual glucose, ethanol, glycerol, and trehalose were quantified by HPLC where applicable using Aminex HPX-87H (5 mM H2SO4, 45 °C) and HPX-87C columns (85 °C). Authentic analytical standards of glucose, ethanol, glycerol, and trehalose were prepared in deionized water and used to generate external calibration curves covering the expected concentration ranges of the fermentation samples. Peak identification was performed by matching retention times with the corresponding standards, and metabolite concentrations were calculated from the calibration curves. All fermentation profiles were obtained from three independent biological replicates and are presented as mean ± SD.
2.7 Reactive oxygen species measurement
Intracellular ROS levels were measured using 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA; Beyotime, China). After freeze–thaw treatment, yeast cells were collected by centrifugation (6000 r/min, 5 min), washed twice with phosphate-buffered saline (PBS), and adjusted to an OD600 of 1.0. Cells were incubated with 10 μM DCFH-DA at 30 °C for 30 min in the dark with gentle mixing. After staining, cells were washed twice with PBS to remove excess probe, resuspended in PBS, and fluorescence intensity was quantified using a microplate reader (excitation 488 nm, emission 525 nm). ROS levels were expressed as relative fluorescence units (RFU)/OD600 to normalize fluorescence signals to biomass. Fluorescence values from unstained cells were used as background controls and subtracted where applicable. For microscopic visualization, stained cells were observed using a fluorescence microscope equipped with a fluorescein isothiocyanate (FITC) filter set (excitation/emission approximately 488/525 nm). Images were acquired under identical exposure and acquisition settings for all samples, and representative bright-field and fluorescence images were collected from independent biological replicates.
2.8 Antioxidant capacity assays
Antioxidant capacity was evaluated using 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity and ferric reducing antioxidant power (FRAP) assays. Yeast samples collected before and after freeze–thaw treatment were harvested, washed twice with PBS, resuspended to a normalized biomass level, and disrupted by sonication on ice. Cell debris was removed by centrifugation, and the supernatants were collected as intracellular extracts. ABTS radical scavenging activity was expressed as scavenging rate (%), and FRAP was expressed as μM Fe(II) L−1 based on calibration with ferrous standards. All measurements were performed using independent biological replicates.
2.9 Field-emission scanning electron microscopy
Yeast cells were prepared for field-emission scanning electron microscopy (FESEM) to assess freeze–thaw-induced ultrastructural changes. Cell suspensions were harvested by centrifugation at 6000 r/min for 5 min, fixed in 5% glutaraldehyde (Electron Microscopy Sciences, Hatfield, PA, USA) at 4 °C overnight, and washed twice with PBS buffer (pH 7.4–7.6). Samples were dehydrated sequentially in 30%, 50%, 70%, 90%, and 100% ethanol, air-dried, mounted on self-adhesive copper tape, and sputter-coated with platinum using a LEICA EM ACE600 coater (Leica Microsystems, Wetzlar, Germany). Imaging was performed using a HITACHI SU8220 cold field-emission scanning electron microscope (Hitachi High-Tech, Tokyo, Japan) at 3 kV, with magnifications ranging from 3000× to 5000×.
2.10 Transcriptome sequencing and differential expression analysis
Transcriptomic analysis was performed using the generation-300 ALE1 population and its corresponding ancestor, Ancestor1 (DBY12007), after the freeze–thaw treatment described above. Three biological replicates were analyzed for each group. Total RNA was extracted, assessed for concentration, purity, and integrity, and used for library construction. Libraries were sequenced on an Illumina NovaSeq 6000 platform using paired-end 150 bp sequencing. Raw reads were filtered to remove adaptor sequences, reads containing excessive ambiguous bases, and low-quality reads. Clean reads were aligned to the Saccharomyces cerevisiae S288C reference genome (R64 release) using HISAT2, and gene-level read counts were generated using featureCounts. Differentially expressed genes were identified using DESeq2. Genes with Benjamini–Hochberg adjusted P < 0.05 and |log2(fold change)| ≥ 1 were considered significantly differentially expressed.
2.11 Intracellular metabolite extraction, LC–HRMS analysis
Untargeted metabolomic profiling was performed using intracellular extracts from the generation-300 ALE1 population and Ancestor1 after freeze–thaw treatment, with three biological replicates per group. Intracellular metabolites were extracted as described above and analyzed by liquid chromatography-high-resolution mass spectrometry (LC–HRMS). Raw data were processed using Compound Discoverer for peak detection, retention-time alignment, feature extraction, and peak-area integration. Signal intensities were normalized by total ion intensity to reduce analytical variation. Pooled quality-control samples, prepared by mixing aliquots from all samples, were injected periodically throughout the analytical sequence. Features with unstable signals in QC samples were removed before downstream analysis. Metabolite annotation was performed against mzCloud, HMDB, Kyoto Encyclopedia of Genes and Genomes (KEGG), and MassBank databases using accurate mass, isotope pattern, retention behavior, and MS/MS fragmentation information. Unless confirmed by authentic standards, metabolites were reported as putatively annotated compounds based on MS/MS database matching. Differential metabolites were selected using VIP > 1 from OPLS-DA, Benjamini–Hochberg adjusted P < 0.05, and fold change ≥ 1.5 or ≤ 0.67.
2.12 Statistical and omics integration analyses
All phenotypic and physiological experiments were performed using independent biological replicates as specified in the corresponding figure legends. Unless otherwise stated, data are presented as mean ± SD. For comparisons involving more than two groups, one-way or two-way ANOVA was used as appropriate, followed by Tukey’s post hoc test. For two-group comparisons, a two-tailed Student’s t-test was used where applicable. Differences were considered statistically significant at P < 0.05.
Principal component analysis (PCA), volcano plots, enrichment analyses, and heatmaps were generated from the processed transcriptomic and metabolomic datasets described in Sections 2.10 and 2.11. Cross-omics integration was performed using normalized gene-expression and metabolite-abundance matrices. Shared pathway analysis was based on KEGG enrichment results from differentially expressed genes and differential metabolites. Gene–metabolite associations, two-way orthogonal partial least squares (O2PLS), canonical correlation analysis (CCA), and correlation analyses were used as exploratory tools to identify coordinated transcriptome–metabolome patterns. These analyses were interpreted as supporting evidence for biological modules rather than direct proof of causal gene–metabolite relationships.
3 Results and Discussion
3.1 Adaptive laboratory evolution improves freeze–thaw survival while preserving growth capacity
Three independent S. cerevisiae evolutionary lineages, ALE1, ALE2, and ALE3, were generated from DBY12007, PGY7, and PGY34, respectively, through repeated freeze–thaw selection. In this manuscript, ALE1–ALE3 refer to the evolved mixed populations collected at the corresponding generation checkpoints, with the generation-300 populations used for endpoint phenotypic and omics analyses. The evolutionary workflow consisted of growth, freeze–thaw exposure, recovery of surviving cells, and serial transfer, with checkpoints at the 0th, 100th, 200th, and 300th generations (Fig. 1A). This design created a sustained selection regime in which cells unable to tolerate repeated freezing and thawing were progressively removed from the population. Across the three backgrounds, viable cell density normalized to OD600 increased over evolutionary time (Fig. 1B), indicating that the evolved populations contained a larger viable fraction per unit biomass. This is important because higher CFU/OD600 reflects improved population robustness rather than simply increased optical density or biomass accumulation.
Selection for freeze–thaw survival did not produce an obvious cost in baseline growth. Doubling times under non-stress YPD conditions remained within a relatively narrow range during evolution (Fig. 1C), and generation-300 populations did not show a systematic growth penalty compared with their corresponding ancestors. This observation is relevant for industrial use because stress-tolerant strains that grow poorly are often unattractive even if they survive a laboratory stress assay [
24]. The freeze–thaw survival phenotype showed a much sharper trajectory than growth. Ancestral populations had very low survival after repeated freeze–thaw treatment, and the 100th and 200th generations showed only partial or inconsistent improvement. At the 300th generation, however, survival increased markedly, reaching approximately 89%–96% in ALE1, 56%–69% in ALE2, and 26%–29% in ALE3 (Fig. 1D). The late, nonlinear gain is consistent with freeze–thaw tolerance behaving as a multicomponent trait that requires several compatible changes, such as improved envelope stability, redox control, and osmotic protection, rather than a single early mutation producing the full phenotype [
25,
26].
Spot assays provided an independent visual confirmation of the CFU-based survival data. Before freeze–thaw treatment, ancestral and evolved lineages showed broadly comparable colony-forming ability across serial dilutions. After freeze–thaw exposure, ancestral strains showed sparse growth or loss of colonies at higher dilutions, whereas generation-300 evolved lineages retained stronger and more uniform colony formation (Fig. 1E). The agreement between normalized viable counts, survival measurements, and spot recovery supports the conclusion that ALE genuinely increased post-thaw viability across multiple genetic backgrounds. The three lineages did not evolve to the same final survival level, but all moved in the same phenotypic direction, showing that repeated freeze–thaw selection was sufficient to select robust populations from both laboratory-related and wild backgrounds.
3.2 Evolved lineages improve frozen-dough baking performance
The practical value of improved freeze–thaw robustness depends on whether the cellular phenotype translates into a food matrix. Before freezing, breads fermented with ancestral strains and corresponding evolved lineages showed similar overall loaf shape and crumb appearance (Fig. 2A), indicating that ALE did not impair basic dough-leavening capacity under non-stressed conditions. This result is consistent with the stable non-stress growth phenotype observed during evolution (Fig. 1C). After frozen storage and thawing, differences became more apparent. Breads prepared with ancestral strains showed reduced height, more compact crumb, and visible structural collapse, whereas breads prepared with evolved lineages retained greater loaf expansion and a more uniform internal structure (Fig. 2A). The visual differences were supported by specific volume and texture profile analysis (Fig. 2B).
After freeze–thaw treatment, specific volume decreased strongly in breads fermented with ancestral strains, whereas evolved lineages maintained higher loaf volume. This effect is biologically plausible because loaf expansion after freezing depends on the ability of yeast cells to survive, recover, and generate CO
2 during proofing. The evolved lineages also produced breads with lower hardness and reduced gumminess and chewiness after freezing (Fig. 2B), which is consistent with better preservation of gas cell structure and less compact crumb. Parameters describing elastic recovery and structural integrity, including springiness, cohesiveness, and resilience, were also better retained in evolved-lineage breads. Frozen dough deterioration involves both yeast injury and dough-matrix changes, including gluten weakening, starch-related changes, and water redistribution [
27,
28]. The present data do not isolate each of these contributions, but they show that improving yeast freeze–thaw robustness is sufficient to produce measurable gains in final bread quality under the tested formulation. Thus, ALE-derived yeast improvement is not only a plate-survival phenotype; it has a direct consequence for frozen dough performance.
3.3 ALE1 shows accelerated post-thaw recovery and stronger fermentation-associated activity
Because the ALE1 evolved lineage showed the strongest survival gain and was derived from the DBY12007 background, it was selected for more detailed physiological and omics analyses. This choice was also practical: DBY12007 has an S288C-related background, which facilitates interpretation of transcriptomic and metabolomic changes, while the phenotypic contrast between Ancestor1 and ALE1 was large enough to support mechanistic analysis [
29]. Before freezing, Ancestor1 and ALE1 showed similar growth curves and overlapping doubling-time distributions (Fig. 3A), confirming that ALE1 did not simply grow faster under normal conditions. After freeze–thaw treatment, however, the two strains separated clearly. Ancestor1 showed a long recovery delay and large replicate-to-replicate variability, whereas ALE1 resumed growth earlier and reached a higher
OD600 within the same observation window (Fig. 3A). This pattern indicates that ALE1 had not only a higher viable fraction but also a more synchronized population recovery state after thawing [
8].
Fermentation-associated measurements across the three strain backgrounds further connected post-thaw recovery with functional metabolism (Fig. 3B). In the DBY12007 background, the difference between Ancestor1 and ALE1 was the most pronounced, matching the survival phenotype and supporting the use of this lineage for subsequent omics analysis. The time-course profiles of pH,
OD600, glucose, and ethanol showed that evolved lineages, especially ALE1, maintained stronger growth and fermentation-associated activity after freeze–thaw treatment. A faster
OD600 increase reflects more efficient cellular recovery, while glucose depletion and ethanol accumulation indicate restoration of fermentative metabolism. The pH profile provides an additional readout of culture acidification and metabolic activity. These parameters are not independent of each other, but together they provide a more complete view of post-thaw physiological function than survival measurements alone. This distinction is important in frozen dough systems because yeast cells must rapidly re-enter fermentation during proofing to sustain CO
2 production, gas-cell expansion, and final product structure. Previous studies on frozen dough and baker’s yeast have similarly shown that freezing-related impairment of yeast activity and fermentation capacity is closely associated with reduced loaf volume and poorer bread quality [
1,
30]. The fermentation data therefore provide a bridge between the population survival phenotype in Fig. 1 and the baking performance in Fig. 2.
3.4 Reduced oxidative burden and altered antioxidant capacity accompany the evolved phenotype
Freeze–thaw stress is closely associated with oxidative imbalance because dehydration, osmotic fluctuations during thawing, membrane perturbation, and delayed metabolic recovery can promote intracellular ROS accumulation. Excessive ROS can further damage cellular components, including membrane lipids, proteins, and nucleic acids, thereby extending recovery time after stress exposure [
5,
31]. To evaluate whether adaptive evolution affected oxidative status after freeze–thaw treatment, intracellular ROS levels were measured using DCFH-DA staining. Across the three genetic backgrounds, ancestral populations showed stronger ROS-associated fluorescence signals than their corresponding evolved populations (Fig. 4A). Quantitation of fluorescence intensity normalized to
OD600 further confirmed lower ROS-associated signals in ALE populations (Fig. 4B), indicating reduced oxidative burden per unit biomass after freeze–thaw treatment. Because DCFH-DA fluorescence reflects the overall oxidative state of cells rather than a specific ROS species or detoxification pathway, these results are interpreted as evidence of altered oxidative status rather than direct proof of enhanced antioxidant mechanisms [
32].
To further characterize changes in redox-related properties, ABTS radical scavenging activity and ferric reducing antioxidant power (FRAP) were measured before and after freeze–thaw treatment (Figs. 4C and 4D). These assays reflect different aspects of chemical antioxidant capacity: ABTS mainly evaluates radical-scavenging activity, whereas FRAP measures reducing capacity based on electron-donating ability [
33,
34]. Accordingly, differences between these measurements are expected because they represent distinct chemical properties rather than a single antioxidant pool. In the present study, evolved populations exhibited altered ABTS and FRAP profiles compared with their ancestral counterparts, with the most evident changes observed after freeze–thaw stress. The higher reducing capacity observed in evolved populations suggests improved maintenance of cellular reducing potential under stress conditions, whereas changes in radical-scavenging activity indicate remodeling of antioxidant-related metabolites or compounds. However, these assays alone cannot identify the specific molecular pathways responsible for these changes.
Together, the ROS measurements and antioxidant capacity assays indicate that adaptive evolution reduced freeze–thaw-associated oxidative burden and reshaped redox-related cellular properties. Rather than reflecting a simple increase in antioxidant activity, the evolved phenotype is more appropriately described as a shift in oxidative balance involving ROS generation, detoxification capacity, and maintenance of reducing equivalents.
3.5 Evolved lineages preserve cell-envelope morphology after freeze–thaw stress
Cell-envelope damage is another major component of freeze–thaw injury. Ice formation, dehydration, and rehydration can disrupt membrane structure, alter cell-wall mechanics, and cause leakage or collapse, which in turn can intensify oxidative damage and delay recovery [
35]. Scanning electron microscope (SEM) images showed clear differences between ancestral and evolved lineages after freeze–thaw treatment across all three backgrounds (Fig. 5). Ancestral cells frequently exhibited irregular outlines, wrinkled or depressed surfaces, and visibly damaged regions, with representative injury sites marked in the figure. In contrast, evolved cells more often retained rounded morphology, smoother surfaces, and clearer cell boundaries at both 3000× and 5000× magnification. The effect was especially evident in the DBY12007/ALE1 pair but was not restricted to that background.
Scanning electron microscopy (SEM) images of evolved lineages and corresponding ancestral strains after freeze–thaw treatment. Images are shown for three genetic backgrounds: DBY12007/ALE1, PGY7/ALE2, and PGY34/ALE3. For each strain pair, representative micrographs are shown at 3000× and 5000× magnification. Red circles indicate representative regions of cell-surface deformation, collapse, or structural damage observed in ancestral cells after freeze–thaw treatment. Evolved lineages exhibited smoother cell surfaces, more regular morphology, and reduced visible envelope disruption compared with their corresponding ancestral strains.
The SEM data should be interpreted as morphological evidence rather than direct chemical proof of membrane remodeling. Nevertheless, the images are highly consistent with the survival, recovery, and ROS results. Cells that better preserve envelope morphology after freeze–thaw treatment would be expected to limit solute leakage, reduce ionic imbalance, and recover membrane function more efficiently during thawing. In this context, the SEM phenotype provides a physical counterpart to the biochemical redox phenotype in Fig. 4 and helps explain why evolved lineages recover more rapidly after stress. The observation across three genetic backgrounds also suggests that preservation of cell-envelope integrity is a recurring outcome of freeze–thaw ALE, even though the degree of survival improvement differs among lineages.
3.6 Transcriptomic profiling identifies regulatory modules associated with freeze–thaw adaptation in ALE1
RNA-seq was used to determine whether the physiological differences between Ancestor1 and ALE1 were accompanied by stable transcriptional remodeling. PCA separated ALE1 from Ancestor1, indicating that ALE1 had established a distinct transcriptomic state (Fig. 6A). Differential expression analysis identified 1706 differentially expressed genes, including 947 genes higher in ALE1 and 759 genes lower in ALE1 (Fig. 6B). The scale of this response indicates that repeated freeze–thaw selection did not simply tune one stress gene or one protective pathway. Instead, evolution shifted multiple transcriptional programs, including transport, carbohydrate utilization, sulfur amino acid metabolism, cell-wall/external-structure functions, and cell-cycle or meiosis-associated processes (Fig. 6C).
Several features of the transcriptomic response are worth emphasizing. Functional module analysis showed that transporter and membrane-exchange genes were enriched among genes higher in ALE1, suggesting that evolved cells may have improved solute transport and membrane-associated exchange during recovery. Carbohydrate utilization and glycolytic rewiring were also prominent, consistent with the stronger post-thaw growth and fermentation profiles in Fig. 3. Sulfur amino acid and redox-related modules were represented as well, linking the transcriptome to the reduced ROS and altered antioxidant phenotype in Fig. 4. In contrast, retrotransposition/RNA-mediated transposition-associated transcripts and several ASP3 family members were lower in ALE1, as further shown in the supplementary heatmap and Gene Ontology (GO) enrichment results (Fig. S1). This does not prove that suppressing these transcripts caused tolerance, but it suggests that ALE1 showed reduced expression of certain high-background or stress-associated transcriptional programs while redirecting capacity toward recovery-related processes.
KEGG enrichment analysis supported the same overall interpretation, with pathways related to cysteine and methionine metabolism, carbohydrate metabolism, transporters, cell cycle-related processes, galactose metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, and amino sugar and nucleotide sugar metabolism appearing among the enriched categories (Fig. 6D). These pathways map well onto the physiology of freeze–thaw recovery: cells must re-establish sugar uptake, restart central carbon metabolism, repair or preserve the envelope, and control redox damage. Similar links between evolutionary engineering and oxidative stress-related transcriptional remodeling have been observed in stress-adapted yeast strains [
20,
36]. In ALE1, the transcriptome indicates a regulatory state biased toward recovery, transport, carbon utilization, and sulfur/redox adaptation rather than toward an isolated canonical stress-response pathway.
3.7 Metabolomic remodeling supports amino acid redistribution, sulfur/redox rewiring, and membrane-associated adaptation
Untargeted metabolomics was then used to determine whether the transcriptional shift in ALE1 was accompanied by biochemical remodeling. PCA of metabolomic profiles showed a clear separation between ALE1 and Ancestor1 (Fig. 7A), and the volcano plot indicated extensive differential metabolite accumulation (Fig. 7B). Chemical superclass analysis showed that differential metabolites were concentrated in organic acids and derivatives, lipids and lipid-like molecules, organic oxygen compounds, organoheterocyclic compounds, nucleosides/nucleotides and analogues, and related compound classes (Fig. 7C). This distribution fits the expected biochemical complexity of freeze–thaw adaptation, where osmotic protection, redox chemistry, nitrogen metabolism, carbon recovery, and membrane properties are all relevant.
Representative metabolites point to three linked adaptive themes (Fig. 7D). First, changes in aspartic acid, L-arginine, and related amino acid derivatives suggest redistribution of nitrogen and amino acid pools. Amino acids such as arginine, proline, glutamate, and aspartate can influence stress protection, nitrogen storage, and recovery metabolism in yeast [
37,
38]. Second, changes in cysteine-glutathione disulfide, gamma-glutamylcysteine, glutathione, and arginylcysteine indicate remodeling of sulfur-containing and glutathione-associated redox metabolism. The observation that glutathione-related species change in different directions is important. It suggests that ALE1 did not simply accumulate more reduced glutathione as a static antioxidant reservoir; rather, glutathione may be turning over through oxidation, conjugation, or related sulfur-metabolism reactions. Third, changes in glycerol 2-phosphate and 1,2-dioctanoyl-sn-glycero-3-phosphocholine implicate glycerophospholipid-related metabolism, which is relevant to membrane fluidity, membrane repair, and cell-envelope stability under freeze–thaw stress [
39,
40].
Additional metabolite KEGG enrichment and heatmap analysis are presented in Fig. S2. Those analyses further support enrichment of amino acid metabolism, glutathione-associated metabolism, carbohydrate metabolism, lipid-associated metabolism, and central carbon metabolism. When interpreted together with Figs. 4 and 5, the metabolome provides a chemical basis for the lower ROS burden and preserved cell morphology observed in evolved lineages. The data also help explain why the antioxidant assays did not all move in the same direction: redox adaptation appears to involve redistribution and turnover of sulfur/glutathione-related metabolites rather than uniform elevation of all antioxidant indicators. The metabolomic profile therefore complements the transcriptomic evidence and identifies concrete biochemical changes underlying the evolved phenotype.
3.8 Cross-omics integration identifies coordinated modules associated with ALE-enhanced freeze–thaw tolerance
To connect transcriptional and metabolic changes more directly, transcriptomic and metabolomic datasets were integrated at the pathway and selected gene-metabolite levels. Shared KEGG analysis showed convergence in cysteine and methionine metabolism, alanine/aspartate/glutamate metabolism, glycolysis/gluconeogenesis, galactose metabolism, amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, transporter-associated processes, and cell-cycle or meiosis-related pathways (Fig. 8A). These shared signals are important because they connect the transcriptional modules in Fig. 6 with the metabolite shifts in Fig. 7. The same biological themes appear repeatedly: recovery of carbon metabolism, redistribution of nitrogen and amino acid pools, sulfur/redox rebalancing, and membrane or transport-associated adaptation.
The selected gene-metabolite association module provides a more detailed view of this coordination (Fig. 8B). Upregulation of
CAR1 together with lower L-arginine suggests that arginine metabolism was rerouted rather than simply expanded [
38]. Upregulation of
GTT1 together with decreased glutathione levels indicates increased glutathione turnover rather than accumulation, reflecting active redox buffering rather than static antioxidant storage, a behavior commonly observed in yeast oxidative stress adaptation [
19]. Downregulation of the ASP3 family together with increased aspartate further indicates remodeling of nitrogen flow and amino acid utilization, consistent with stress-induced reprogramming of amino acid pools. In parallel, changes in
DAK2,
MAL12,
MAL32,
HXT2,
HXT8, and
GAL2 align with altered sugar transport and glycolytic entry, supporting faster metabolic reactivation after thawing, in agreement with known roles of carbohydrate metabolism in frozen dough fermentation performance [
1]. Additional enzyme-metabolite coupling, O2PLS, and CCA analyses are provided in Fig. S3. These statistical associations support cross-omics coordination but are interpreted as supporting evidence rather than direct causal relationships, consistent with systems biology frameworks for multi-layer data interpretation.
Based on these integrated signals, ALE1 adaptation can be organized into four functional modules (Fig. 8C). The first module involves enhanced carbon uptake and recovery metabolism, supported by upregulation of sugar transporters (
HXT2,
HXT8,
GAL2,
MAL12,
MAL32) and altered glycolytic intermediates such as dihydroxyacetone phosphate, consistent with faster recovery of central carbon metabolism after stress. The second module reflects nitrogen redistribution and amino acid remodeling, involving coordinated changes in ASP3 family genes,
CAR1, and amino acids including aspartate, asparagine, arginine, and glutamate, indicating reallocation of nitrogen resources during post-thaw recovery. The third module involves sulfur amino acid and redox rewiring, where increased expression of
MET2,
MET17,
CYS3, and
GTT1, together with altered glutathione-related metabolites, including cysteine–glutathione disulfide, arginylcysteine, and gamma-glutamylcysteine, indicates dynamic redox cycling rather than simple antioxidant accumulation, consistent with yeast oxidative stress response mechanisms [
31]. The fourth module comprises membrane remodeling and transporter-supported adaptation, supported by transporter-related transcriptional changes and lipid-associated metabolites such as glycerol 2-phosphate, 3-hydroxybutyrylcarnitine, and 1,2-dioctanoyl-sn-glycero-3-phosphocholine, consistent with lipid-mediated stress adaptation processes [
40].
Together, these modules support a working model in which ALE-enhanced freeze–thaw tolerance is associated with coordinated remodeling of carbon recovery, nitrogen redistribution, sulfur/redox balance, and membrane-related adaptation. This multi-layer remodeling is consistent with ALE1’s improved survival, lower oxidative burden, preserved morphology, faster post-thaw recovery, and enhanced frozen-dough performance [
15].
4 Conclusions
This study demonstrates that adaptive laboratory evolution provides a practical evolutionary engineering strategy for improving the freeze–thaw robustness of Saccharomyces cerevisiae used in frozen-dough fermentation. After 300 freeze–thaw selection cycles, evolved yeast populations showed markedly improved post-thaw survival while maintaining baseline growth capacity. These cellular improvements were translated into frozen-dough functionality, as evolved populations supported better loaf expansion and texture retention after frozen storage. Physiological analyses further showed faster post-thaw recovery, stronger fermentation-associated activity, reduced oxidative burden, altered redox capacity, and improved preservation of cell-envelope morphology.
Transcriptomic, metabolomic, and cross-omics analyses of ALE1 supported a working model in which the evolved phenotype is associated with coordinated remodeling across four major modules: carbon uptake and recovery metabolism, nitrogen redistribution and amino acid remodeling, sulfur/glutathione-associated redox balance, and membrane-associated adaptation. These modules provide a useful framework for understanding how cellular stress adaptation may contribute to improved frozen-dough performance. However, the present omics analyses identify associations rather than direct causal mechanisms. Targeted genetic validation, biochemical perturbation experiments, and whole-genome sequencing of evolved populations or isolated clones will be needed to identify the mutations and regulatory changes responsible for the observed phenotype. Nevertheless, this work establishes a food-relevant ALE strategy and an integrated omics framework for developing robust microbial starters for frozen food processing.
The Author(s) 2026. This article is published by Higher Education Press.
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