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Record W3046139269 · doi:10.1128/mbio.00986-20

A Cytoplasmic Heme Sensor Illuminates the Impacts of Mitochondrial and Vacuolar Functions and Oxidative Stress on Heme-Iron Homeostasis in Cryptococcus neoformans

2020· article· en· W3046139269 on OpenAlexaff
Gaurav Bairwa, Eddy Sánchez‐León, Eunsoo Do, Won Hee Jung, James W. Kronstad

Bibliographic record

VenuemBio · 2020
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNational Institute of Allergy and Infectious DiseasesNational Research Foundation of Korea
KeywordsHemeCryptococcus neoformansOxidative stressBiologyMitochondrionArtemisininBiochemistryAntifungal drugHeminOxidative phosphorylationCell biologyChemistryPlasmodium falciparumMicrobiologyMalariaAntifungalImmunologyEnzyme

Abstract

fetched live from OpenAlex

Invasive fungal diseases are increasing in frequency, and new drug targets and antifungal drugs are needed to bolster therapy. The mechanisms by which pathogens obtain critical nutrients such as iron from heme during host colonization represent a promising target for therapy. In this study, we employed a fluorescent heme sensor to investigate heme homeostasis in Cryptococcus neoformans . We demonstrated that endocytosis is a key aspect of heme acquisition and that vacuolar and mitochondrial functions are important in regulating the pool of available heme in cells. Stress generated by oxidative conditions impacts the heme pool, as do the drugs artemisinin and metformin; these drugs have heme-related activities and are in clinical use for malaria and diabetes, respectively. Overall, our study provides insights into mechanisms of fungal heme acquisition and demonstrates the utility of the heme sensor for drug characterization in support of new therapies for fungal diseases.

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 score0.002
Threshold uncertainty score0.004

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)0.0000.000

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 head0.253
Teacher spread0.237 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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