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Record W4413872243 · doi:10.18331/brj2025.12.3.5

Metabolic engineering of the non-conventional yeast Kluyveromyces marxianus for enhancing the biosynthesis of succinic acid

2025· article· en· W4413872243 on OpenAlexvenueno aff
Ziyun Gu, Yongsheng Tang, Xiulai Chen

Bibliographic record

VenueBiofuel Research Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesKey Laboratory of Industrial BiotechnologyNational Natural Science Foundation of China
KeywordsKluyveromyces marxianusYeastMetabolic engineeringKluyveromycesSuccinic acidBiosynthesisBiochemistryChemistrySaccharomyces cerevisiaeEnzyme

Abstract

fetched live from OpenAlex

Succinic acid is a crucial four-carbon dicarboxylic acid with widespread applications in the detergent, food, and pharmaceutical industries. However, its microbial production on a large scale is limited by the utilization of neutralizing agents and high cooling costs. In this study, we conducted metabolic engineering of the thermotolerant and acid-tolerant yeast Kluyveromyces marxianus to facilitate the efficient biosynthesis of succinic acid at high temperature and low pH. A robust genetic manipulation platform for K. marxianus was established by developing gene editing tools, characterizing neutral integration sites, and identifying endogenous promoters. Leveraging this efficient platform, we systematically constructed and optimized the biosynthetic pathway of succinic acid through many metabolic engineering strategies, such as the knockout of byproduct pathways, the redistribution of carbon flux, and the enhancement of the succinic acid transport system. Finally, the engineered strain K. marxianus KmSA12 was able to produce 50.6 g/L succinic acid with a yield of 0.31 g/g and productivity of 0.42 g/L/h in a 5-L bioreactor. These results demonstrate the potential of K. marxianus as a promising platform for the large-scale production of various organic acids.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.296
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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