Adaptive laboratory evolution with ethionine identifies novel genetic determinants for enhanced protein and methionine accumulation in Saccharomyces cerevisiae

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Here, we applied ethionine resistance–mediated adaptive laboratory evolution (ALE) to generate strains with improved resistance to this toxic methionine analog. Stepwise adaptation enabled growth at ethionine concentrations of up to 0.50 mM and yielded strains with progressively higher intracellular methionine levels and improved protein production efficiency. Whole-genome sequencing identified nonsynonymous SNPs in 32 genes, of which nine candidates were functionally validated. CRISPR/Cas9-based editing demonstrated that mutations in MDE1 and JJJ1 directly elevated free methionine levels, whereas most other mutations increased overall protein accumulation. Functional annotations linked these genes to RNA processing, protein degradation, methionine salvage, and amino acid uptake, highlighting RNA processing as a major target for global protein enhancement. These findings reveal that ethionine resistance–mediated ALE induces multifactorial adaptations. They also provide new insights into protein biosynthesis regulation and lay a foundation for future engineering of high-performance yeast strains. Saccharomyces cerevisiae single-cell protein adaptive laboratory evolution ethionine resistance single nucleotide polymorphism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Single-cell protein (SCP) is gaining recognition as a sustainable alternative source of protein that can contribute to addressing pressing global challenges such as population growth, climate change, and food security [ 1 , 2 ]. Given its microbial origin, SCP offers distinct advantages over conventional agriculture and livestock production, including rapid production, reduced land and water requirements, and lower greenhouse gas emissions [ 3 , 4 ]. Recent scenario-based modeling has further indicated that substituting even 20% of the global ruminant meat consumption with microbial protein by 2050 could substantially ease environmental pressures, halving projected deforestation and associated CO 2 emissions, while also lowering methane emissions [ 5 ]. Saccharomyces cerevisiae stands out among microbial SCP sources for its substantial protein content (30–50%) and generally recognized as safe (GRAS) status [ 6 ]. S. cerevisiae biomass is also a rich source of functional nutrients such as β-glucan, glutathione, and B-complex vitamins, which further enhance its overall nutritional value [ 7 , 8 , 9 ]. Nonetheless, compared to bacterial SCP, S. cerevisiae –derived SCP has a low overall protein yield and less favorable essential amino acid profile, particularly limited levels of sulfur-containing essential amino acids such as methionine [ 7 , 10 ]. Given the vast number of genes implicated in regulating protein accumulation in yeast, targeted genetic engineering remains highly challenging [ 11 , 12 ]. Consequently, some studies have employed random mutagenesis to generate mutants with enhanced protein content, but this approach has been found to be limited by low reproducibility and the difficulty of identifying desired variants within large mutant libraries [ 13 , 14 ]. In light of these limitations, the present study employed adaptive laboratory evolution (ALE) as an alternative strategy to systematically obtain S. cerevisiae strains with improved protein and methionine content [ 15 , 16 , 17 ]. In this approach, ethionine, a toxic methionine analog [ 17 , 18 ], was used as a selective pressure to enrich for mutants with enhanced protein metabolism (Fig. 1 A). Ethionine resistance can arise from diverse underlying mechanisms and may include alterations in amino acid metabolism, protein synthesis, or stress-response pathways [ 19 , 20 ]. Ethionine toxicity mainly arises from its competitive inhibition of S-adenosylmethionine (SAM) synthetase, which interferes with SAM biosynthesis and methylation-dependent processes [ 21 ]. Under prolonged ethionine stress, cells may acquire adaptive mutations that alleviate this inhibition, thereby enhancing methionine availability and protein production (Fig. 1 B). As expected, the evolved ethionine-resistant strains exhibited higher levels of both methionine and total protein. Furthermore, to uncover the genetic determinants associated with this enhanced protein accumulation, whole-genome sequencing was performed using next-generation sequencing (NGS). From the single nucleotide polymorphisms (SNPs) identified by NGS, select mutations were individually introduced into the parental strain using CRISPR–Cas9 genome editing to assess their effects. This inverse metabolic engineering approach enabled easy identification and validation of genetic targets linked to increased protein production, thereby paving the way for rational design of S. cerevisiae strains with enhanced protein content for SCP production. Methods Strains and plasmids Escherichia coli TOP10 (Invitrogen, Thermo Fisher Scientific, Carlsbad, CA, USA) was used for plasmid construction, and S. cerevisiae D452-2 ( MATα , leu2 , his3 , ura3 , and can1 ) [ 22 ] was used for SCP production. All strains and plasmids used in this study are listed in Table S2. Ethionine resistance-mediated ALE To determine the growth-inhibiting concentration of ethionine, cells were cultivated in YSC medium lacking methionine and supplemented with varying concentrations of ethionine (0.005–0.2 mM). Cultures were incubated at 30°C with shaking at 250 rpm, and cell growth was monitored. Passage cultures were performed in methionine-free ethionine-containing YSC medium. At each passage, cells were inoculated at 1% (v/v) into fresh medium. Ethionine concentrations in the medium were increased stepwise from 0.15 mM to 0.5 mM in 0.05 mM or 0.1 mM increments depending on the observed growth rate. Cultures were incubated at 30°C with agitation at 250 rpm, and aliquots were preserved as glycerol stocks at − 80°C at the end of each passage. Genetic manipulation Gene cloning and gRNA plasmid construction for the CRISPR/Cas9 system were performed using the NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs, Ipswich, MA, USA) following the manufacturer's instructions. The primer sets used to amplify guide RNA (gRNA) plasmids are listed in Table S3. S. cerevisiae strains carrying SNPs were generated using a CRISPR/Cas9-based genome editing system as described previously [ 23 ]. In brief, gRNA plasmids and repair DNA fragments (amplified with primers listed in Table S3) were cotransformed into the D452-2 strain harboring pCas9_AUR [ 23 ] to yield the recombinant strains (Table S2). Media and culture conditions E. coli strains were cultivated in Luria–Bertani (LB) medium (10 g/L tryptone, 5 g/L yeast extract, and 10 g/L NaCl) containing 50 µg/mL ampicillin. S. cerevisiae strains were precultured in YP20D medium at 30°C and 250 rpm for 48 h. The precultured cells were harvested and inoculated into baffled flasks containing 100 mL of YP50D medium at an initial OD 600 of 1.0. Batch fermentations were performed at 30°C and 250 rpm for 48 h. Genome sequencing The genomes of S. cerevisiae strains were sequenced and analyzed following the procedures described in a previous study [ 14 ]. In brief, genomic DNA (gDNA) was extracted from the parental S. cerevisiae D452-2 strain and from the evolved fifth-passage strain, using a gDNA extraction kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. The purified gDNA samples were submitted to Macrogen (Seoul, Republic of Korea) for library preparation and whole-genome resequencing. Sequencing libraries were generated and analyzed using the HiSeq 4000 platform (Illumina, San Diego, CA, USA). Genetic variations in the evolved strain were identified using the CLC Genomics Workbench (originally by CLC bio, Aarhus, Denmark; now part of QIAGEN Aarhus). Analytical methods Cell growth was measured at OD 600 using a spectrophotometer (OPTIZEN POP, Mecasys Co., Ltd., Daejeon, Republic of Korea). Amino acid profiling was performed using an HPLC system (UltiMate 3000, Thermo Fisher Scientific, USA) equipped with an INNO C18 column (YoungJin Biochrom Co., Ltd., Republic of Korea). Cell lysates were prepared and subjected to precolumn derivatization with 9-fluorenylmethoxycarbonyl chloride and o -phthalaldehyde, followed by fluorescence detection under previously described conditions [ 13 , 14 ]. Methionine content was determined following a precolumn derivatization method reported previously [ 24 ]. In brief, the cell lysates were allowed to react with 2,4-dinitrofluorobenzene (DNFB, 10 g/L) and NaHCO₃ (0.5 M, pH 9.0) at 60°C for 60 min in the dark. The mixtures were subsequently neutralized with KH₂PO₄ buffer (0.01 M, pH 7.0) and subjected to HPLC analysis using an UltiMate 3000 HPLC system (Thermo Fisher Scientific, USA) equipped with an Inertsil ODS-3 column (Shimadzu, Kyoto, Japan) under the chromatographic conditions as previously reported [ 13 , 25 ]. Statistical analysis Statistical analyses were performed using SPSS Statistics v.28.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean ± standard deviation. One-way analysis of variance was performed, and statistical significance was assessed using Tukey’s honestly significant difference (HSD) test at a significance level of p < 0.05. In addition, an independent samples Student’s t -test was performed to evaluate differences between two groups with statistical significance defined as p < 0.05. Results and Discussion Ethionine resistance–mediated ALE for selection of evolved strains with high methionine content To establish the selection pressure for ethionine resistance–based ALE, we first determined the ethionine concentration that severely inhibited the growth of the parental S. cerevisiae D452-2 strain. Cells were cultivated in yeast synthetic complete (YSC) medium lacking methionine (6.7 g/L yeast nitrogen base without amino acids, 1.92 g/L complete supplement mixture without methionine, and 20 g/L glucose) and supplemented with varying concentrations of ethionine (0.005–0.2 mM). As shown in Fig. 2 A, no detectable growth of D452-2 was observed at 0.2 mM ethionine; however, at a lower ethionine concentration of 0.15 mM, growth was suppressed, with an extended lag phase of approximately 60 h. Based on these observations, 0.15 mM ethionine was selected as the initial concentration for subsequent ALE experiments. ALE proceeded via stepwise increases in ethionine concentration. In passage 1, S. cerevisiae cultures were grown in YSC medium supplemented with 0.15 mM ethionine. To establish passage 2, these cultures were transferred into fresh YSC medium containing 0.20 mM ethionine. Notably, despite the increased ethionine concentration, the lag phase in passage 2 was shorter than that in the initial cultivation (Fig. 2 B), indicating that repeated culturing in defined medium with ethionine allowed resistant mutants to emerge and dominate. Through seven successive passages with progressively increasing ethionine concentrations, S. cerevisiae adapted to growing in YSC medium containing 0.50 mM ethionine, with a lag phase of approximately 30 h by the final passage. However, the maximum dry cell weight at this concentration was lower than that observed at lower ethionine concentrations. From passages 3 to 7, cultures were first precultured in YP20D medium (10 g/L yeast extract, 20 g/L Bacto™ Peptone, and 20 g/L glucose) and then transferred to YP50D medium (10 g/L yeast extract, 20 g/L Bacto™ Peptone, and 50 g/L glucose). For each passage, intracellular free methionine levels were quantified. These levels increased steadily over successive ALE passages (Fig. 3 A), peaking after passage 6 at 0.57%—a 60% increase relative to the parental D452-2 strain. Enhanced protein production efficiency following ethionine resistance–mediated ALE We next hypothesized that the increased intracellular methionine observed as a consequence of ethionine-mediated ALE would result in a concomitant increase in the overall protein content in the evolved strains, given methionine’s role as both the initiating amino acid in protein synthesis and a key metabolite in sulfur amino acid metabolism [ 26 , 27 ]. In addition, the continuous selective pressure applied during ALE was likely to have remodeled the translational machinery and amino acid sensing pathways. Potential adaptations could include increased specificity of methionyl-tRNA synthetase, upregulation of ribosomal proteins to restore translational fidelity, and reprogramming of TORC1- and GCN4-mediated signaling to promote the expression of genes involved in translation, tRNA charging, and amino acid metabolism [ 28 , 29 , 30 ]. Together, these adaptations were expected to enhance protein synthesis, a possibility we verified by quantifying the total protein content in the evolved strains. The relative protein content and concentration across evolved strains were measured using the Bradford assay, revealing increased protein levels relative to the parental strain (Fig. 3 B and Fig. S1 ). In particular, the passage-4 strain exhibited a 2.5-fold higher protein content and 2.0-fold higher protein concentration than the parental D452-2 strain, whereas the passage-5 strain showed 2.4-fold and 2.0-fold increases, respectively. The Bradford assay was used solely to compare relative protein levels across strains; the exact protein concentrations were determined using an HPLC-based amino acid profiling method. Relative to the parental strain, passage-4 and passage-5 strains exhibited elevated levels of most amino acids (Fig. 4 A). Consequently, the total protein content of these strains was 55% and 53%, corresponding to 43% and 40% increases compared to the parental strain, respectively (Fig. 4 B). It should be noted, however, that the apparently similar increases in total protein and free methionine contents should be interpreted with caution due to methodological limitations. Amino acids exist in two forms in cells: free amino acids, which are present as individual molecules, and constituent amino acids, which are bound within proteins. Accordingly, total amino acids represent the sum of both forms. Amino acid profiling, which was conducted for total amino acid analysis using conventional sulfuric acid hydrolysis [ 31 ], causes degradation of sulfur-containing residues such as methionine, resulting in the absence of detectable methionine (Fig. 4 A). Accordingly, in this study, only free methionine was quantified separately by a dedicated HPLC-based method, which accurately measures intact methionine molecules (Fig. 3 A). Therefore, if alternative hydrolysis methods that better preserve methionine (e.g., methane sulfonic acid hydrolysis) [ 32 , 33 ] had been applied for compositional amino acid analysis, the measured increase in total methionine content could have been substantially greater than that of the overall protein content. Nevertheless, despite these methodological limitations, the evolved populations clearly exhibited substantial increases in both intracellular methionine and total protein content, thereby validating our initial hypothesis that ethionine-mediated adaptive evolution enhanced global protein synthesis through metabolic and translational remodeling. Identification and functional validation of candidate SNPs driving adaptation under ethionine selection To test the hypothesis that ethionine-mediated evolution introduced genetic variations enhancing protein production, we performed a comparative whole-genome analysis of the parental D452-2 strain and the fifth-passage strain using next-generation sequencing. SNP variations causing amino acid substitutions were identified in 32 genes distributed across various chromosomes of the fifth-passage strain (Table S1 ). Because analyzing all the 32 SNP-containing genes for associations with the observed protein and methionine increases would be labor-intensive and time consuming, we focused on nine candidate genes most strongly associated with these traits, namely, POP4 , RRP3 , SWT21 , GRC3 , JJJ1 , DDI1 , ATG34 , MDE1 , and TAT2 (Table 1 ). The SNPs identified in these genes produced the following missense mutations: (1) K14T and R105K in POP4, (2) V203I in RRP3, (3) V194M in SWT21, (4) D171N in GRC3, (5) P541S in JJJ1, (6) R61K in DDI1, (7) V385M in ATG34, (8) D101N and V119L in MDE1, and (9) H50N and D509G in TAT2. Functional annotations revealed that these genes participate in RNA processing ( POP4 , RRP3 , GRC3 , SWT21 , and JJJ1 ), protein degradation ( DDI1 and ATG34 ), the methionine salvage pathway ( MDE1 ), and amino acid uptake ( TAT2 ). Table 1 Key candidate gene mutations associated with increased protein content in the fifth-passage strain Gene Nucleotide change Amino acid change Function POP4 (YBR257W) A 41 → C G 341 → A Lys 14 → Thr Arg 105 → Lys Ribonuclease complex subunit required for tRNA maturation and rRNA processing RRP3 (YHR065C) G 607 → A Val 203 → Ile DEAD-box RNA helicase involved in pre-rRNA processing and ribosome biogenesis SWT21 (YNL187W) G 580 → A Val 194 → Met Spliceosome regulator involved in mRNA splicing GRC3 (YLL035W) G 511 → A Asp 171 → Asn Polynucleotide 5'-hydroxyl-kinase involved in rRNA processing and termination of RNA polymerase I transcription JJJ1 (YNL227C) C 1621 → T Pro 541 → Ser J-domain co-chaperone (Hsp40 family) involved in rRNA processing and ribosomal large subunit biogenesis and nuclear export DDI1 (YER143W) G 182 → A Arg 61 → Lys Ubiquitin-like protease involved in protein degradation ATG34 (YOL083W) G 1153 → A Val 385 → Met Autophagy receptor involved in selective transport of endocytic vesicle cargo to vacuoles MDE1 (YJR024C) G 301 → A G 355 → C Asp 101 → Asn Val 119 → Leu Methionine salvage pathway enzyme involved in methionine recycling TAT2 (YOL020W) C 148 → A A 1256 → A His 50 → Asn Asp 509 → Gly High-affinity tryptophan permease mediating uptake of aromatic amino acids Finally, we tested whether the SNPs in the nine candidate genes indeed contributed to the observed increases in methionine and protein content: each SNP was introduced individually into the parental strain using CRISPR/Cas9-based genome editing, except in MDE1 , where two closely located SNPs were introduced together (Table S1 ). As seen in Fig. 5 , all mutations except for the K14T substitution in POP4 led to an increase in protein content in S. cerevisiae . Notably, the MDE1* and JJJ1* strains, into which the MDE1 and JJJ1 SNPs had been introduced, exhibited 11% and 10% higher free methionine levels, respectively, compared with the parental strain. These findings suggests that, among the nine candidate genes, only MDE1 and JJJ1 directly increased free methionine levels; the other mutations predominantly enhanced protein accumulation. Nonetheless, these other mutations may also have contributed to elevated total methionine content because free amino acid assays cannot distinguish between pre-existing free methionine and protein-derived methionine. This illustrates that adaptation to ethionine is multifactorial, with some mutations enhancing free methionine pools and others promoting protein biosynthesis, which indirectly increases methionine levels. These findings suggest that ethionine-mediated adaptive evolution imposed selective pressure not only on genes directly associated with methionine metabolism but also on genes involved in other cellular processes. Mutations in RNA processing genes ( POP4 , RRP3 , GRC3 , SWT21 , and JJJ1 ) are particularly noteworthy because they affect ribosome biogenesis and RNA stability, thereby influencing translational efficiency and global protein synthesis [ 34 ]. Similarly, mutations in protein degradation genes ( DDI1 and ATG34 ) may have reduced protein degradation through changes in ubiquitin–proteasome activity or autophagic turnover. Furthermore, mutations in MDE1, a key enzyme of the methionine salvage pathway, indicate adaptive rewiring of sulfur metabolism [ 35 ]. By maintaining methionine availability under conditions of ethionine stress, these mutations may have indirectly contributed to both higher free methionine levels and increased protein biosynthesis. Finally, mutations in TAT2, a high-affinity tryptophan permease involved in amino acid uptake, point to another mechanism that supports growth under ethionine stress [ 36 ]. Enhanced uptake of specific amino acids may have relieved metabolic bottlenecks and helped meet anabolic demand, indirectly supporting methionine utilization and protein synthesis. In sum, ethionine-mediated ALE did not simply enrich for improved methionine biosynthesis but instead promoted global adaptations across RNA processing, protein degradation, sulfur metabolism, and amino acid uptake. Together, these changes likely adjusted proteostasis and acted synergistically to increase both methionine and total protein levels. Conclusion This study demonstrates that ethionine supplementation not only boosts methionine production but also enhances protein production in S. cerevisiae . The study also uncovered molecular mechanisms linking between ethionine resistance with increased protein biosynthesis. Comparative genomic and functional analyses revealed that genes involved in RNA processing, protein degradation, the methionine salvage pathway, and amino acid uptake play central roles, with RNA processing–related genes particularly showing frequent mutations. These findings suggest that regulatory control of RNA processing may be key to enhancing protein production in yeast. Future studies will focus on examining the effects of the identified SNPs on protein activity and testing optimal SNP combinations to further increase protein yields. Collectively, the results of this study lay a foundation for the development of high-performance yeast strains with improved protein output and offer new insights into the molecular mechanisms governing protein biosynthesis in eukaryotic cells. Declarations Data Availability Data are available from the corresponding author upon request. Funding Declaration This study was financially supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) (RS-2025-02218354) and by the R&D program of MOTIE/KEIT (RS-2025-02307907). This research was also supported by a grant from the National Research Foundation of Korea (RS-2024-00440975), funded by the Korean Ministry of Science, ICT, and Future Planning. Competing Interests The authors declare no competing interests. Author Contribution T.H.L.: Methodology, Investigation. S.D.: Methodology, Investigation. H.L.: Writing – original draft preparation, Data Curation. K.L.: Formal analysis. J.S.: Formal analysis. Y.P.: Conceptualization. S.K.: Supervision, Writing – original draft preparation. Data Availability Data are available from the corresponding author upon request. References Li YP, Ahmadi F, Kariman K, Lackner M. 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Supplementary Files EthionineevolutionSupplementarydata251009JournalofBiologicalEngineering.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2026 Read the published version in Journal of Biological Engineering → Version 1 posted Editorial decision: Revision requested 26 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 30 Dec, 2025 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 29 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8474260","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572725530,"identity":"f747ba7e-d8df-4e9a-a003-4a24886af68e","order_by":0,"name":"Tae Hoon Lee","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Tae","middleName":"Hoon","lastName":"Lee","suffix":""},{"id":572725531,"identity":"c8019328-f233-4ff1-a6a0-6225bdf02833","order_by":1,"name":"Sang-Hun Do","email":"","orcid":"","institution":"Chung-Ang 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University","correspondingAuthor":true,"prefix":"","firstName":"Sun-Ki","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-12-29 15:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8474260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8474260/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13036-026-00652-x","type":"published","date":"2026-03-07T16:00:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100181215,"identity":"31ad68dd-0d16-4f92-a668-8c446f17ffeb","added_by":"auto","created_at":"2026-01-13 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07:57:29","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90664,"visible":true,"origin":"","legend":"","description":"","filename":"9cb5eef7df184114b0192a24e23108ce1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/43ddfdd07add9dfeb77738cd.xml"},{"id":100181242,"identity":"a9c30216-2d0d-4d6b-af34-10f946c29a0c","added_by":"auto","created_at":"2026-01-13 19:34:23","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101724,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/4fe1e8a32d1cb37d656f0e21.html"},{"id":100181214,"identity":"a232f1ed-8cb1-4878-958d-3f535c02d687","added_by":"auto","created_at":"2026-01-13 19:34:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":774195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdaptive laboratory evolution (ALE) under ethionine stress and mechanistic model of S-adenosylmethionine (SAM) synthetase inhibition. \u003c/strong\u003e(A) Schematic of ethionine resistance–mediated ALE, SNP identification by whole-genome sequencing, and functional validation using CRISPR/Cas9 editing. (B) Model of competitive inhibition of SAM synthetase by ethionine (Eth). In wild-type cells, SAM synthetase is competitively inhibited by Eth, leading to S-adenosylethionine formation and growth inhibition. In contrast, in evolved strains with enhanced methionine (Met) producing capability, this inhibition is alleviated, enabling SAM synthesis and restoring growth.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/c6a760bd7bb5edc86544997e.jpg"},{"id":100369234,"identity":"e83fbce8-e99b-4cd5-9339-e5f2e7193cf5","added_by":"auto","created_at":"2026-01-16 07:58:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":593534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSaccharomyces cerevisiae \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eD452-2 under ethionine stress and during ALE. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eGrowth curves of the parental D452-2 strain cultivated in yeast synthetic complete (YSC) medium lacking methionine and supplemented with varying concentrations of ethionine (0–0.2 mM). (B) Growth curves of evolved strains obtained from the first passage to the seventh passage under stepwise increments of ethionine concentrations (0.15–0.50 mM). Error bars represent standard deviations from biological replicates (n ≥ 2).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/0bce7461af48f2123ee44fe9.jpg"},{"id":100181218,"identity":"4a6b1f87-5f52-4afa-82fb-129e6b95330e","added_by":"auto","created_at":"2026-01-13 19:34:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":577410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of free methionine content (A) and relative protein content (B) of parental and evolved strains. \u003c/strong\u003eFree methionine content was measured in the parental D452-2 strain and evolved strains obtained from the third passage to the seventh passage of ethionine-mediated ALE. Relative protein content was determined using the Bradford assay. Results are the mean of n ≥ 2 experiments, and error bars indicate the standard deviation. Different letters represent significantly different means (Tukey’s honestly significant difference tests, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/8f7c8df5f21fafe15161f0fa.jpg"},{"id":100368499,"identity":"ac4602f3-e765-4e8b-bf71-efcf1a5e0b67","added_by":"auto","created_at":"2026-01-16 07:58:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":627008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of individual amino acid content (A) and total amino acid content (B) of parental and evolved strains. \u003c/strong\u003eAmino acid profiling was performed for the parental D452-2 strain and evolved strains obtained after the fourth and fifth passages of ethionine-mediated ALE. Results are the mean of two experiments, and error bars indicate the standard deviation. Different letters represent significantly different means (Tukey’s honestly significant difference tests, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/92a34dd186bb5094fd5b40b8.jpg"},{"id":100369516,"identity":"5c596134-e383-49c8-876f-10761e104f8d","added_by":"auto","created_at":"2026-01-16 07:59:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":794928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of free methionine content (A) and relative protein content (B) of the parental strain and SNP-introduced strains. \u003c/strong\u003eFree methionine and protein concentrations were measured in the parental D452-2 strain and recombinant strains carrying SNPs in specific candidate genes. The specific amino acid substitutions corresponding to each SNP-introduced strain are summarized in Table 1. Results are the mean of two experiments, and error bars indicate the standard deviation. Asterisks (*) indicate statistically significant differences compared with the parental strain (Student’s t-test, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/9105461de1f2fac13d642187.jpg"},{"id":104251778,"identity":"eab377ab-805b-4be2-a0af-e0a257a4e1fc","added_by":"auto","created_at":"2026-03-09 16:15:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4365231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/015189d5-f7c3-44cc-a824-49397a17dd61.pdf"},{"id":100181221,"identity":"78cf75e8-ce9c-42e6-8d9f-78c9ec167cdb","added_by":"auto","created_at":"2026-01-13 19:34:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325133,"visible":true,"origin":"","legend":"","description":"","filename":"EthionineevolutionSupplementarydata251009JournalofBiologicalEngineering.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8474260/v1/4bbbb2f821d45be9fd4461e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive laboratory evolution with ethionine identifies novel genetic determinants for enhanced protein and methionine accumulation in Saccharomyces cerevisiae","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSingle-cell protein (SCP) is gaining recognition as a sustainable alternative source of protein that can contribute to addressing pressing global challenges such as population growth, climate change, and food security [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given its microbial origin, SCP offers distinct advantages over conventional agriculture and livestock production, including rapid production, reduced land and water requirements, and lower greenhouse gas emissions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent scenario-based modeling has further indicated that substituting even 20% of the global ruminant meat consumption with microbial protein by 2050 could substantially ease environmental pressures, halving projected deforestation and associated CO\u003csub\u003e2\u003c/sub\u003e emissions, while also lowering methane emissions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e stands out among microbial SCP sources for its substantial protein content (30\u0026ndash;50%) and generally recognized as safe (GRAS) status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eS. cerevisiae\u003c/em\u003e biomass is also a rich source of functional nutrients such as β-glucan, glutathione, and B-complex vitamins, which further enhance its overall nutritional value [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nonetheless, compared to bacterial SCP, \u003cem\u003eS. cerevisiae\u003c/em\u003e\u0026ndash;derived SCP has a low overall protein yield and less favorable essential amino acid profile, particularly limited levels of sulfur-containing essential amino acids such as methionine [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the vast number of genes implicated in regulating protein accumulation in yeast, targeted genetic engineering remains highly challenging [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, some studies have employed random mutagenesis to generate mutants with enhanced protein content, but this approach has been found to be limited by low reproducibility and the difficulty of identifying desired variants within large mutant libraries [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In light of these limitations, the present study employed adaptive laboratory evolution (ALE) as an alternative strategy to systematically obtain \u003cem\u003eS. cerevisiae\u003c/em\u003e strains with improved protein and methionine content [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this approach, ethionine, a toxic methionine analog [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], was used as a selective pressure to enrich for mutants with enhanced protein metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Ethionine resistance can arise from diverse underlying mechanisms and may include alterations in amino acid metabolism, protein synthesis, or stress-response pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Ethionine toxicity mainly arises from its competitive inhibition of S-adenosylmethionine (SAM) synthetase, which interferes with SAM biosynthesis and methylation-dependent processes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Under prolonged ethionine stress, cells may acquire adaptive mutations that alleviate this inhibition, thereby enhancing methionine availability and protein production (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs expected, the evolved ethionine-resistant strains exhibited higher levels of both methionine and total protein. Furthermore, to uncover the genetic determinants associated with this enhanced protein accumulation, whole-genome sequencing was performed using next-generation sequencing (NGS). From the single nucleotide polymorphisms (SNPs) identified by NGS, select mutations were individually introduced into the parental strain using CRISPR\u0026ndash;Cas9 genome editing to assess their effects. This inverse metabolic engineering approach enabled easy identification and validation of genetic targets linked to increased protein production, thereby paving the way for rational design of \u003cem\u003eS. cerevisiae\u003c/em\u003e strains with enhanced protein content for SCP production.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStrains and plasmids\u003c/h2\u003e \u003cp\u003e \u003cem\u003eEscherichia coli\u003c/em\u003e TOP10 (Invitrogen, Thermo Fisher Scientific, Carlsbad, CA, USA) was used for plasmid construction, and \u003cem\u003eS. cerevisiae\u003c/em\u003e D452-2 (\u003cem\u003eMATα\u003c/em\u003e, \u003cem\u003eleu2\u003c/em\u003e, \u003cem\u003ehis3\u003c/em\u003e, \u003cem\u003eura3\u003c/em\u003e, and \u003cem\u003ecan1\u003c/em\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used for SCP production. All strains and plasmids used in this study are listed in Table S2.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthionine resistance-mediated ALE\u003c/h3\u003e\n\u003cp\u003eTo determine the growth-inhibiting concentration of ethionine, cells were cultivated in YSC medium lacking methionine and supplemented with varying concentrations of ethionine (0.005\u0026ndash;0.2 mM). Cultures were incubated at 30\u0026deg;C with shaking at 250 rpm, and cell growth was monitored.\u003c/p\u003e \u003cp\u003ePassage cultures were performed in methionine-free ethionine-containing YSC medium. At each passage, cells were inoculated at 1% (v/v) into fresh medium. Ethionine concentrations in the medium were increased stepwise from 0.15 mM to 0.5 mM in 0.05 mM or 0.1 mM increments depending on the observed growth rate. Cultures were incubated at 30\u0026deg;C with agitation at 250 rpm, and aliquots were preserved as glycerol stocks at \u0026minus;\u0026thinsp;80\u0026deg;C at the end of each passage.\u003c/p\u003e\n\u003ch3\u003eGenetic manipulation\u003c/h3\u003e\n\u003cp\u003eGene cloning and gRNA plasmid construction for the CRISPR/Cas9 system were performed using the NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs, Ipswich, MA, USA) following the manufacturer's instructions. The primer sets used to amplify guide RNA (gRNA) plasmids are listed in Table S3.\u003c/p\u003e \u003cp\u003e \u003cem\u003eS. cerevisiae\u003c/em\u003e strains carrying SNPs were generated using a CRISPR/Cas9-based genome editing system as described previously [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In brief, gRNA plasmids and repair DNA fragments (amplified with primers listed in Table S3) were cotransformed into the D452-2 strain harboring pCas9_AUR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to yield the recombinant strains (Table S2).\u003c/p\u003e\n\u003ch3\u003eMedia and culture conditions\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eE. coli\u003c/em\u003e strains were cultivated in Luria\u0026ndash;Bertani (LB) medium (10 g/L tryptone, 5 g/L yeast extract, and 10 g/L NaCl) containing 50 \u0026micro;g/mL ampicillin. \u003cem\u003eS. cerevisiae\u003c/em\u003e strains were precultured in YP20D medium at 30\u0026deg;C and 250 rpm for 48 h. The precultured cells were harvested and inoculated into baffled flasks containing 100 mL of YP50D medium at an initial OD\u003csub\u003e600\u003c/sub\u003e of 1.0. Batch fermentations were performed at 30\u0026deg;C and 250 rpm for 48 h.\u003c/p\u003e\n\u003ch3\u003eGenome sequencing\u003c/h3\u003e\n\u003cp\u003eThe genomes of \u003cem\u003eS. cerevisiae\u003c/em\u003e strains were sequenced and analyzed following the procedures described in a previous study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In brief, genomic DNA (gDNA) was extracted from the parental \u003cem\u003eS. cerevisiae\u003c/em\u003e D452-2 strain and from the evolved fifth-passage strain, using a gDNA extraction kit (Zymo Research, Irvine, CA, USA) according to the manufacturer\u0026rsquo;s instructions. The purified gDNA samples were submitted to Macrogen (Seoul, Republic of Korea) for library preparation and whole-genome resequencing. Sequencing libraries were generated and analyzed using the HiSeq 4000 platform (Illumina, San Diego, CA, USA). Genetic variations in the evolved strain were identified using the CLC Genomics Workbench (originally by CLC bio, Aarhus, Denmark; now part of QIAGEN Aarhus).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical methods\u003c/h2\u003e \u003cp\u003eCell growth was measured at OD\u003csub\u003e600\u003c/sub\u003e using a spectrophotometer (OPTIZEN POP, Mecasys Co., Ltd., Daejeon, Republic of Korea). Amino acid profiling was performed using an HPLC system (UltiMate 3000, Thermo Fisher Scientific, USA) equipped with an INNO C18 column (YoungJin Biochrom Co., Ltd., Republic of Korea). Cell lysates were prepared and subjected to precolumn derivatization with 9-fluorenylmethoxycarbonyl chloride and \u003cem\u003eo\u003c/em\u003e-phthalaldehyde, followed by fluorescence detection under previously described conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethionine content was determined following a precolumn derivatization method reported previously [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In brief, the cell lysates were allowed to react with 2,4-dinitrofluorobenzene (DNFB, 10 g/L) and NaHCO₃ (0.5 M, pH 9.0) at 60\u0026deg;C for 60 min in the dark. The mixtures were subsequently neutralized with KH₂PO₄ buffer (0.01 M, pH 7.0) and subjected to HPLC analysis using an UltiMate 3000 HPLC system (Thermo Fisher Scientific, USA) equipped with an Inertsil ODS-3 column (Shimadzu, Kyoto, Japan) under the chromatographic conditions as previously reported [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS Statistics v.28.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. One-way analysis of variance was performed, and statistical significance was assessed using Tukey\u0026rsquo;s honestly significant difference (HSD) test at a significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, an independent samples Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was performed to evaluate differences between two groups with statistical significance defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthionine resistance\u0026ndash;mediated ALE for selection of evolved strains with high methionine content\u003c/h2\u003e \u003cp\u003eTo establish the selection pressure for ethionine resistance\u0026ndash;based ALE, we first determined the ethionine concentration that severely inhibited the growth of the parental \u003cem\u003eS. cerevisiae\u003c/em\u003e D452-2 strain. Cells were cultivated in yeast synthetic complete (YSC) medium lacking methionine (6.7 g/L yeast nitrogen base without amino acids, 1.92 g/L complete supplement mixture without methionine, and 20 g/L glucose) and supplemented with varying concentrations of ethionine (0.005\u0026ndash;0.2 mM). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, no detectable growth of D452-2 was observed at 0.2 mM ethionine; however, at a lower ethionine concentration of 0.15 mM, growth was suppressed, with an extended lag phase of approximately 60 h. Based on these observations, 0.15 mM ethionine was selected as the initial concentration for subsequent ALE experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eALE proceeded via stepwise increases in ethionine concentration. In passage 1, \u003cem\u003eS. cerevisiae\u003c/em\u003e cultures were grown in YSC medium supplemented with 0.15 mM ethionine. To establish passage 2, these cultures were transferred into fresh YSC medium containing 0.20 mM ethionine. Notably, despite the increased ethionine concentration, the lag phase in passage 2 was shorter than that in the initial cultivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating that repeated culturing in defined medium with ethionine allowed resistant mutants to emerge and dominate. Through seven successive passages with progressively increasing ethionine concentrations, \u003cem\u003eS. cerevisiae\u003c/em\u003e adapted to growing in YSC medium containing 0.50 mM ethionine, with a lag phase of approximately 30 h by the final passage. However, the maximum dry cell weight at this concentration was lower than that observed at lower ethionine concentrations.\u003c/p\u003e \u003cp\u003eFrom passages 3 to 7, cultures were first precultured in YP20D medium (10 g/L yeast extract, 20 g/L Bacto\u0026trade; Peptone, and 20 g/L glucose) and then transferred to YP50D medium (10 g/L yeast extract, 20 g/L Bacto\u0026trade; Peptone, and 50 g/L glucose). For each passage, intracellular free methionine levels were quantified. These levels increased steadily over successive ALE passages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), peaking after passage 6 at 0.57%\u0026mdash;a 60% increase relative to the parental D452-2 strain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEnhanced protein production efficiency following ethionine resistance\u0026ndash;mediated ALE\u003c/h2\u003e \u003cp\u003eWe next hypothesized that the increased intracellular methionine observed as a consequence of ethionine-mediated ALE would result in a concomitant increase in the overall protein content in the evolved strains, given methionine\u0026rsquo;s role as both the initiating amino acid in protein synthesis and a key metabolite in sulfur amino acid metabolism [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, the continuous selective pressure applied during ALE was likely to have remodeled the translational machinery and amino acid sensing pathways. Potential adaptations could include increased specificity of methionyl-tRNA synthetase, upregulation of ribosomal proteins to restore translational fidelity, and reprogramming of TORC1- and GCN4-mediated signaling to promote the expression of genes involved in translation, tRNA charging, and amino acid metabolism [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Together, these adaptations were expected to enhance protein synthesis, a possibility we verified by quantifying the total protein content in the evolved strains.\u003c/p\u003e \u003cp\u003eThe relative protein content and concentration across evolved strains were measured using the Bradford assay, revealing increased protein levels relative to the parental strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In particular, the passage-4 strain exhibited a 2.5-fold higher protein content and 2.0-fold higher protein concentration than the parental D452-2 strain, whereas the passage-5 strain showed 2.4-fold and 2.0-fold increases, respectively. The Bradford assay was used solely to compare relative protein levels across strains; the exact protein concentrations were determined using an HPLC-based amino acid profiling method. Relative to the parental strain, passage-4 and passage-5 strains exhibited elevated levels of most amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Consequently, the total protein content of these strains was 55% and 53%, corresponding to 43% and 40% increases compared to the parental strain, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt should be noted, however, that the apparently similar increases in total protein and free methionine contents should be interpreted with caution due to methodological limitations. Amino acids exist in two forms in cells: free amino acids, which are present as individual molecules, and constituent amino acids, which are bound within proteins. Accordingly, total amino acids represent the sum of both forms. Amino acid profiling, which was conducted for total amino acid analysis using conventional sulfuric acid hydrolysis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], causes degradation of sulfur-containing residues such as methionine, resulting in the absence of detectable methionine (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Accordingly, in this study, only free methionine was quantified separately by a dedicated HPLC-based method, which accurately measures intact methionine molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Therefore, if alternative hydrolysis methods that better preserve methionine (e.g., methane sulfonic acid hydrolysis) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] had been applied for compositional amino acid analysis, the measured increase in total methionine content could have been substantially greater than that of the overall protein content. Nevertheless, despite these methodological limitations, the evolved populations clearly exhibited substantial increases in both intracellular methionine and total protein content, thereby validating our initial hypothesis that ethionine-mediated adaptive evolution enhanced global protein synthesis through metabolic and translational remodeling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and functional validation of candidate SNPs driving adaptation under ethionine selection\u003c/h2\u003e \u003cp\u003eTo test the hypothesis that ethionine-mediated evolution introduced genetic variations enhancing protein production, we performed a comparative whole-genome analysis of the parental D452-2 strain and the fifth-passage strain using next-generation sequencing. SNP variations causing amino acid substitutions were identified in 32 genes distributed across various chromosomes of the fifth-passage strain (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Because analyzing all the 32 SNP-containing genes for associations with the observed protein and methionine increases would be labor-intensive and time consuming, we focused on nine candidate genes most strongly associated with these traits, namely, \u003cem\u003ePOP4\u003c/em\u003e, \u003cem\u003eRRP3\u003c/em\u003e, \u003cem\u003eSWT21\u003c/em\u003e, \u003cem\u003eGRC3\u003c/em\u003e, \u003cem\u003eJJJ1\u003c/em\u003e, \u003cem\u003eDDI1\u003c/em\u003e, \u003cem\u003eATG34\u003c/em\u003e, \u003cem\u003eMDE1\u003c/em\u003e, and \u003cem\u003eTAT2\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The SNPs identified in these genes produced the following missense mutations: (1) K14T and R105K in POP4, (2) V203I in RRP3, (3) V194M in SWT21, (4) D171N in GRC3, (5) P541S in JJJ1, (6) R61K in DDI1, (7) V385M in ATG34, (8) D101N and V119L in MDE1, and (9) H50N and D509G in TAT2. Functional annotations revealed that these genes participate in RNA processing (\u003cem\u003ePOP4\u003c/em\u003e, \u003cem\u003eRRP3\u003c/em\u003e, \u003cem\u003eGRC3\u003c/em\u003e, \u003cem\u003eSWT21\u003c/em\u003e, and \u003cem\u003eJJJ1\u003c/em\u003e), protein degradation (\u003cem\u003eDDI1\u003c/em\u003e and \u003cem\u003eATG34\u003c/em\u003e), the methionine salvage pathway (\u003cem\u003eMDE1\u003c/em\u003e), and amino acid uptake (\u003cem\u003eTAT2\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey candidate gene mutations associated with increased protein content in the fifth-passage strain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmino acid change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePOP4 (YBR257W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003csup\u003e41\u003c/sup\u003e \u0026rarr; C\u003c/p\u003e \u003cp\u003eG\u003csup\u003e341\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLys\u003csup\u003e14\u003c/sup\u003e \u0026rarr; Thr\u003c/p\u003e \u003cp\u003eArg\u003csup\u003e105\u003c/sup\u003e \u0026rarr; Lys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRibonuclease complex subunit required for tRNA maturation and rRNA processing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRRP3 (YHR065C)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e607\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVal\u003csup\u003e203\u003c/sup\u003e \u0026rarr; Ile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDEAD-box RNA helicase involved in pre-rRNA processing and ribosome biogenesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSWT21 (YNL187W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e580\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVal\u003csup\u003e194\u003c/sup\u003e \u0026rarr; Met\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpliceosome regulator involved in mRNA splicing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGRC3 (YLL035W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e511\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsp\u003csup\u003e171\u003c/sup\u003e \u0026rarr; Asn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolynucleotide 5'-hydroxyl-kinase involved in rRNA processing and termination of RNA polymerase I transcription\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJJJ1 (YNL227C)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csup\u003e1621\u003c/sup\u003e \u0026rarr; T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePro\u003csup\u003e541\u003c/sup\u003e \u0026rarr; Ser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJ-domain co-chaperone (Hsp40 family) involved in rRNA processing and ribosomal large subunit biogenesis and nuclear export\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDDI1 (YER143W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e182\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArg\u003csup\u003e61\u003c/sup\u003e \u0026rarr; Lys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUbiquitin-like protease involved in protein degradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATG34 (YOL083W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e1153\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVal\u003csup\u003e385\u003c/sup\u003e \u0026rarr; Met\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutophagy receptor involved in selective transport of endocytic vesicle cargo to vacuoles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMDE1 (YJR024C)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003csup\u003e301\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003cp\u003eG\u003csup\u003e355\u003c/sup\u003e \u0026rarr; C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsp\u003csup\u003e101\u003c/sup\u003e \u0026rarr; Asn\u003c/p\u003e \u003cp\u003eVal\u003csup\u003e119\u003c/sup\u003e\u0026rarr; Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethionine salvage pathway enzyme involved in methionine recycling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTAT2 (YOL020W)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csup\u003e148\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003cp\u003eA\u003csup\u003e1256\u003c/sup\u003e \u0026rarr; A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHis\u003csup\u003e50\u003c/sup\u003e \u0026rarr; Asn\u003c/p\u003e \u003cp\u003eAsp\u003csup\u003e509\u003c/sup\u003e \u0026rarr; Gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh-affinity tryptophan permease mediating uptake of aromatic amino acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, we tested whether the SNPs in the nine candidate genes indeed contributed to the observed increases in methionine and protein content: each SNP was introduced individually into the parental strain using CRISPR/Cas9-based genome editing, except in \u003cem\u003eMDE1\u003c/em\u003e, where two closely located SNPs were introduced together (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all mutations except for the K14T substitution in POP4 led to an increase in protein content in \u003cem\u003eS. cerevisiae\u003c/em\u003e. Notably, the MDE1* and JJJ1* strains, into which the \u003cem\u003eMDE1\u003c/em\u003e and \u003cem\u003eJJJ1\u003c/em\u003e SNPs had been introduced, exhibited 11% and 10% higher free methionine levels, respectively, compared with the parental strain. These findings suggests that, among the nine candidate genes, only \u003cem\u003eMDE1\u003c/em\u003e and \u003cem\u003eJJJ1\u003c/em\u003e directly increased free methionine levels; the other mutations predominantly enhanced protein accumulation. Nonetheless, these other mutations may also have contributed to elevated total methionine content because free amino acid assays cannot distinguish between pre-existing free methionine and protein-derived methionine. This illustrates that adaptation to ethionine is multifactorial, with some mutations enhancing free methionine pools and others promoting protein biosynthesis, which indirectly increases methionine levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings suggest that ethionine-mediated adaptive evolution imposed selective pressure not only on genes directly associated with methionine metabolism but also on genes involved in other cellular processes. Mutations in RNA processing genes (\u003cem\u003ePOP4\u003c/em\u003e, \u003cem\u003eRRP3\u003c/em\u003e, \u003cem\u003eGRC3\u003c/em\u003e, \u003cem\u003eSWT21\u003c/em\u003e, and \u003cem\u003eJJJ1\u003c/em\u003e) are particularly noteworthy because they affect ribosome biogenesis and RNA stability, thereby influencing translational efficiency and global protein synthesis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, mutations in protein degradation genes (\u003cem\u003eDDI1\u003c/em\u003e and \u003cem\u003eATG34\u003c/em\u003e) may have reduced protein degradation through changes in ubiquitin\u0026ndash;proteasome activity or autophagic turnover. Furthermore, mutations in MDE1, a key enzyme of the methionine salvage pathway, indicate adaptive rewiring of sulfur metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. By maintaining methionine availability under conditions of ethionine stress, these mutations may have indirectly contributed to both higher free methionine levels and increased protein biosynthesis. Finally, mutations in TAT2, a high-affinity tryptophan permease involved in amino acid uptake, point to another mechanism that supports growth under ethionine stress [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Enhanced uptake of specific amino acids may have relieved metabolic bottlenecks and helped meet anabolic demand, indirectly supporting methionine utilization and protein synthesis. In sum, ethionine-mediated ALE did not simply enrich for improved methionine biosynthesis but instead promoted global adaptations across RNA processing, protein degradation, sulfur metabolism, and amino acid uptake. Together, these changes likely adjusted proteostasis and acted synergistically to increase both methionine and total protein levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that ethionine supplementation not only boosts methionine production but also enhances protein production in \u003cem\u003eS. cerevisiae\u003c/em\u003e. The study also uncovered molecular mechanisms linking between ethionine resistance with increased protein biosynthesis. Comparative genomic and functional analyses revealed that genes involved in RNA processing, protein degradation, the methionine salvage pathway, and amino acid uptake play central roles, with RNA processing\u0026ndash;related genes particularly showing frequent mutations. These findings suggest that regulatory control of RNA processing may be key to enhancing protein production in yeast. Future studies will focus on examining the effects of the identified SNPs on protein activity and testing optimal SNP combinations to further increase protein yields. Collectively, the results of this study lay a foundation for the development of high-performance yeast strains with improved protein output and offer new insights into the molecular mechanisms governing protein biosynthesis in eukaryotic cells.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eData are available from the corresponding author upon request.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study was financially supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) (RS-2025-02218354) and by the R\u0026amp;D program of MOTIE/KEIT (RS-2025-02307907). This research was also supported by a grant from the National Research Foundation of Korea (RS-2024-00440975), funded by the Korean Ministry of Science, ICT, and Future Planning.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.H.L.: Methodology, Investigation. S.D.: Methodology, Investigation. H.L.: Writing \u0026ndash; original draft preparation, Data Curation. K.L.: Formal analysis. J.S.: Formal analysis. Y.P.: Conceptualization. S.K.: Supervision, Writing \u0026ndash; original draft preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi YP, Ahmadi F, Kariman K, Lackner M. 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Accurate and efficient amino acid analysis for protein quantification using hydrophilic interaction chromatography coupled tandem mass spectrometry. Plant Methods. 2019;15(1):46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega AF, Zhao H, Van Amburgh ME. Development and validation of a method for hydrolysis and analysis of amino acids in ruminant feeds, tissue, and milk using isotope dilution Z-HILIC coupled with electrospray ionization triple quadrupole LC-MS/MS. J Agric Food Chem. 2024;72(1):833\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Bazzini AA. Translation and mRNA stability control. Annu Rev Biochem. 2023;92:227\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirkov I, Norbeck J, Gustafsson L, Albers E. A complete inventory of all enzymes in the eukaryotic methionine salvage pathway. FEBS J. 2008;275(16):4111\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz SJ, van 't Klooster JS, Bianchi F, Poolman B. Growth inhibition by amino acids in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. Microorganisms. 2021;9(1):7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-biological-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbie","sideBox":"Learn more about [Journal of Biological Engineering](http://jbioleng.biomedcentral.com/)","snPcode":"13036","submissionUrl":"https://submission.nature.com/new-submission/13036/3","title":"Journal of Biological Engineering","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Saccharomyces cerevisiae, single-cell protein, adaptive laboratory evolution, ethionine resistance, single nucleotide polymorphism","lastPublishedDoi":"10.21203/rs.3.rs-8474260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8474260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnhancing methionine and protein content in \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e is essential for its use as single-cell protein. Here, we applied ethionine resistance\u0026ndash;mediated adaptive laboratory evolution (ALE) to generate strains with improved resistance to this toxic methionine analog. Stepwise adaptation enabled growth at ethionine concentrations of up to 0.50 mM and yielded strains with progressively higher intracellular methionine levels and improved protein production efficiency. Whole-genome sequencing identified nonsynonymous SNPs in 32 genes, of which nine candidates were functionally validated. CRISPR/Cas9-based editing demonstrated that mutations in \u003cem\u003eMDE1\u003c/em\u003e and \u003cem\u003eJJJ1\u003c/em\u003e directly elevated free methionine levels, whereas most other mutations increased overall protein accumulation. Functional annotations linked these genes to RNA processing, protein degradation, methionine salvage, and amino acid uptake, highlighting RNA processing as a major target for global protein enhancement. These findings reveal that ethionine resistance\u0026ndash;mediated ALE induces multifactorial adaptations. They also provide new insights into protein biosynthesis regulation and lay a foundation for future engineering of high-performance yeast strains.\u003c/p\u003e","manuscriptTitle":"Adaptive laboratory evolution with ethionine identifies novel genetic determinants for enhanced protein and methionine accumulation in Saccharomyces cerevisiae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 19:34:18","doi":"10.21203/rs.3.rs-8474260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-26T16:26:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T12:47:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T05:58:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T00:22:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T06:09:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300465261588786187487844350925165407993","date":"2026-01-09T18:00:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330215328584068335183586267137051286170","date":"2026-01-09T14:25:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325510266710983351102291511151459441393","date":"2026-01-09T05:32:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22375204241189860908036726351040797248","date":"2026-01-09T05:05:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133254537139948465994073495775212492322","date":"2026-01-09T04:37:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109156957089669161828728498966010441728","date":"2026-01-09T02:59:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321347151057106767239560341410431213074","date":"2026-01-09T01:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22520951774788385118477141999163317372","date":"2026-01-08T23:52:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76050599250937893035198456554886673966","date":"2026-01-08T18:58:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T18:07:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T13:54:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T13:53:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Biological Engineering","date":"2025-12-29T15:04:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-biological-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbie","sideBox":"Learn more about [Journal of Biological Engineering](http://jbioleng.biomedcentral.com/)","snPcode":"13036","submissionUrl":"https://submission.nature.com/new-submission/13036/3","title":"Journal of Biological Engineering","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03499e8d-bd64-4738-961e-2495acb511d5","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:12:12+00:00","versionOfRecord":{"articleIdentity":"rs-8474260","link":"https://doi.org/10.1186/s13036-026-00652-x","journal":{"identity":"journal-of-biological-engineering","isVorOnly":false,"title":"Journal of Biological Engineering"},"publishedOn":"2026-03-07 16:00:11","publishedOnDateReadable":"March 7th, 2026"},"versionCreatedAt":"2026-01-13 19:34:18","video":"","vorDoi":"10.1186/s13036-026-00652-x","vorDoiUrl":"https://doi.org/10.1186/s13036-026-00652-x","workflowStages":[]},"version":"v1","identity":"rs-8474260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8474260","identity":"rs-8474260","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}