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Record W2100992169 · doi:10.1093/infdis/jit029

The Mannoprotein Cig1 Supports Iron Acquisition From Heme and Virulence in the Pathogenic Fungus Cryptococcus neoformans

2013· article· en· W2100992169 on OpenAlexafffund
Brigitte Cadieux, Tianshun Lian, Guanggan Hu, Joyce Wang, Carmelo Biondo, Giuseppe Teti, Victor Liu, M.E.P. Murphy, A. Louise Creagh, James W. Kronstad

Bibliographic record

VenueThe Journal of Infectious Diseases · 2013
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsCryptococcus neoformansHemeVirulenceMicrobiologyVirulence factorMutantBiologyFungal proteinExtracellularBiochemistryFungusGeneEnzyme

Abstract

fetched live from OpenAlex

Iron acquisition is critical for virulence of the human pathogenic fungus Cryptococcus neoformans. The cryptococcal transcript for the extracellular mannoprotein Cig1 is highly regulated by iron and abundant in iron-starved cells, suggesting a role in iron acquisition. Indeed, loss of Cig1 resulted in delayed growth on heme at physiological pH. Expression of CIG1 is regulated by the pH-responsive transcription factor Rim101, and loss of Rim101 also impaired growth on heme. A cig1Δ mutant was less susceptible than the wild-type strain to noniron metalloporphyrins, further indicating a role for Cig1 in heme uptake. Recombinant Cig1 exhibited the absorbance spectrum of a heme-binding protein upon heme titration, and Cig1 may therefore function as a hemophore at the cell surface. Cig1 contributed to virulence in a mouse model of cryptococcosis but only in a mutant that also lacked the high-affinity iron uptake system. Overall, Cig1-mediated heme uptake is a potential therapeutic target in C. neoformans.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations122
Published2013
Admission routes2
Has abstractyes

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