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Record W3199559230 · doi:10.1111/febs.16209

Haem oxygenase limits <i>Mycobacterium marinum</i> infection‐induced detrimental ferrostatin‐sensitive cell death in zebrafish

2021· article· en· W3199559230 on OpenAlexaff
Kaiming Luo, Roland Stocker, Warwick J. Britton, Kazu Kikuchi, Stefan H. Oehlers

Bibliographic record

VenueFEBS Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsInstitute of Infection and Immunity
FundersNational Health and Medical Research CouncilNSW Ministry of HealthUniversity of Sydney
KeywordsMycobacterium marinumZebrafishHMOX1PathogenBiologyMicrobiologyMycobacteriumHost (biology)XiphophorusCell biologyHeme oxygenaseHemeGeneGeneticsBiochemistryBacteriaEnzymeFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Iron homeostasis is essential for both sides of the host-pathogen interface. Restricting access of iron slows bacterial growth while iron is also a necessary cofactor for host immunity. Haem oxygenase 1 (HMOX1) is a critical regulator of iron homeostasis that catalyses the liberation of iron during degradation of haem. It is also a stress-responsive protein that can be rapidly upregulated and confers protection to the host. Although a protective role of HMOX1 has been demonstrated in a variety of diseases, the role of HMOX1 in Mycobacterium tuberculosis infection is equivocal across experiments with different host-pathogen combinations. Here, we use the natural host-pathogen pairing of the zebrafish-Mycobacterium marinum infection platform to study the role of zebrafish haem oxygenase in mycobacterial infection. We identify zebrafish Hmox1a as the relevant functional paralog of mammalian HMOX1 and demonstrate a conserved role for Hmox1a in protecting the host from M. marinum infection. Using genetic and chemical tools, we show zebrafish Hmox1a protects the host against M. marinum infection by reducing infection-induced iron accumulation and ferrostatin-sensitive cell death.

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 categoriesMeta-epidemiology (narrow)
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 score0.051
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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.

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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

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