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Record W4322008608 · doi:10.1186/s12934-023-02046-w

Combining manipulation of integration loci and secretory pathway on expression of an Aspergillus niger glucose oxidase gene in Trichoderma reesei

2023· article· en· W4322008608 on OpenAlexaff
Wangli Ji, Xiaolu Wang, Xiaoqing Liu, Yuan Wang, Fangui Liu, Bo Xu, Huiying Luo, Tao Tu, Wei Zhang, Xinxin Xu, Xiaoyun Su

Bibliographic record

VenueMicrobial Cell Factories · 2023
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsBiotechnology Research Institute
FundersNational Key Research and Development Program of ChinaChinese Academy of SciencesInstitute of Microbiology, Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsTrichoderma reeseiAspergillus nigerGeneBiologyLocus (genetics)HeterologousHeterologous expressionSecretory proteinTranscription factorCellulaseCell biologyGeneticsBiochemistryEnzymeRecombinant DNA

Abstract

fetched live from OpenAlex

Trichoderma reesei (T. reesei) is well-known for its excellent ability to secret a large quantity of cellulase. However, unlike the endogenous proteins, little is known about the molecular mechanisms governing heterologous protein production. Herein, we focused on the integration loci and the secretory pathway, and investigated their combinatorial effects on heterologous gene expression in T. reesei using a glucose oxidase from Aspergillus niger as a model protein. Integration in the cel3c locus was more efficient than the cbh1 locus in expressing the AnGOx by increasing the transcription of AnGOx in the early stage. In addition, we discovered that interruption of the cel3c locus has an additional effect by increasing the expression of the secretory pathway component genes. Accordingly, overexpressing three secretory pathway component genes, that were snc1, sso2, and rho3, increased AnGOx expression in the cbh1 transformant but not in the cel3c transformant.

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 score0.003
Threshold uncertainty score0.446

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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