MétaCan
Menu
Back to cohort
Record W2098446935 · doi:10.1128/iai.00018-13

Congenic Strains for Genetic Analysis of Virulence Traits in Cryptococcus gattii

2013· article· en· W2098446935 on OpenAlexaboutno aff
Pinkuan Zhu, Bing Zhai, Xiaorong Lin, Alexander Idnurm

Bibliographic record

VenueInfection and Immunity · 2013
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthChina Scholarship Council
KeywordsBiologyCryptococcus gattiiVirulenceCongenicCryptococcusMicrobiologyGeneticsCryptococcosisVirologyGene

Abstract

fetched live from OpenAlex

Cryptococcus gattii is responsible for a large outbreak of potentially fatal disease that started in the late 1990s on Vancouver Island, Canada. How this fungus and the outbreak isolates in particular cause disease in immunocompetent people is unknown, with differing hypotheses. To explore genetic contributions, a pair of congenic a and α mating type strains was generated by a series of 11 backcrosses to introgress the MAT locus from a nonoutbreak strain into the background of strain R265, isolated from a Vancouver Island patient. The congenic pair was used to investigate three traits: mitochondrial inheritance, the effect of the MAT alleles on virulence, and the impact of a predicted virulence factor on pathogenicity. The two congenic strains show the same virulence in different models of cryptococcosis and equivalent levels of competition in coinfection assays. These results rule out a role of the MAT locus and mitochondrial genotype as major virulence factors in the outbreak strains. Disruption of Bwc2, a light-dependent transcription factor, resulted in reduced virulence, consistent with a similar function in the related species Cryptococcus neoformans. The C. gattii congenic strains represent a new resource for exploring the evolution of virulence in the C. neoformans-C. gattii clade.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.408

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 head0.027
GPT teacher head0.302
Teacher spread0.274 · 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 designObservational
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

Citations30
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInfection and ImmunitySame topicFungal Infections and StudiesFrench-language works237,207