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Record W3038376635 · doi:10.1139/cjm-2019-0456

Screening of thermosensitive autolytic mutant brewer’s yeast and transcriptomic analysis of heat stress response

2020· article· en· W3038376635 on OpenAlexvenueno aff
Mingfang Zhang, Jinjing Wang, Chengtuo Niu, Feiyun Zheng, Chunfeng Liu, Qi Li

Bibliographic record

VenueCanadian Journal of Microbiology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
Fundersnot available
KeywordsAutolysis (biology)YeastMutantBiochemistryPentose phosphate pathwayHexoseGlycogenChemistrySaccharomyces cerevisiaeMutagenesisBiologyGlycolysisFood scienceMetabolismEnzymeGene

Abstract

fetched live from OpenAlex

Brewer's yeast has been widely used in the food industry, and the autolysates thereof are increasingly being studied for their valuable nutritional compositions. Yeast autolysis is most affected by medium composition and temperature. In this study, a thermosensitive autolytic brewer's yeast P-510 was obtained with atmospheric and room temperature plasma mutagenesis plus 5-bromo-chloro-3-indolyl phosphate screening. The mutant rapidly autolyzed at 37 °C and the autolysates contained more active components and showed higher antioxidant activities compared with that of the parental strain, which indicated that the mutant's autolysates can potentially be used as functional food and nutritional ingredients. Transcriptomic analysis of the mutant and parental strains at 28 and 37 °C suggested that thermosensitive autolysis of P-510 was probably caused by mitochondrial disfunction, glycogen metabolic flux of glycolysis and pentose phosphate pathway disorder, as well as hexose transport inhibition. The results revealed the important role of mitochondrial metabolism and glycogen utilization regulation in heat stress response of yeast.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.010
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.197
Teacher spread0.188 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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