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Record W2417027682 · doi:10.1007/s00203-016-1240-6

A set of isogenic auxotrophic strains for constructing multiple gene deletion mutants and parasexual crossings in Aspergillus niger

2016· article· en· W2417027682 on OpenAlexaff
Jing Niu, Mark Arentshorst, Felix Seelinger, Arthur F. J. Ram, Jean Ouedraogo

Bibliographic record

VenueArchives of Microbiology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsConcordia University
FundersChina Scholarship Council
KeywordsAuxotrophyBiologyGeneticsGeneMutantSelectable markerGenomeMutagenesisAspergillus nigerPlasmidBiochemistry

Abstract

fetched live from OpenAlex

To construct a set of isogenic auxotrophic strains in Aspergillus niger suited for creating multiple gene deletion mutants and executing parasexual crossings, we have combined mutations in genes involved in colour pigmentation (fwnA and olvA) with well-selectable auxotrophic markers (pyrG, nicB, argB, and adeA). All markers, except for the pyrG marker, were introduced by targeted deletion, omitting UV mutagenesis of the strains. Aspergillus oryzae orthologous genes of the argB, nicB, and adeA markers were used as heterologous selection markers, and all markers were shown to complement to respective auxotrophic A. niger mutants. A quadruple auxotrophic marker was further constructed suitable for multiple gene deletions. Genome sequencing of two auxotrophic colour mutants JN3.2 (olvA::pyrG, argB::hygB) and JN6.2 (olvA::pyrG, nicB::hygB) revealed four SNPs between them in non-coding regions, indicating a high level of isogenicity between both strains. The availability of near-isogenic complementary auxotrophic colour mutants facilitates the selection of diploids and the isolation of haploid segregants from the diploid using the parasexual cycle.

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 score0.024
Threshold uncertainty score0.331

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 studies0.0000.001
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 head0.268
Teacher spread0.252 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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