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Record W4410334596 · doi:10.1101/2025.05.12.653471

Met32 governs transcriptional control of sulfur metabolic flexibility and resistance to reactive sulfur species in the human fungal pathogen <i>Candida albicans</i>

2025· preprint· en· W4410334596 on OpenAlexafffund
Anagha C.T. Menon, Faïza Tebbji, Azadeh Alikashani, Antony T. Vincent

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversité de MontréalUniversité LavalMontreal Heart Institute
FundersCanadian Institutes of Health ResearchUniversity of California, San FranciscoNatural Sciences and Engineering Research Council of CanadaInstitut de Cardiologie de MontréalFondation Institut de Cardiologie de MontréalCanada Foundation for InnovationConcordia UniversityJulius-Maximilians-Universität WürzburgFonds de Recherche du Québec - SantéUniversité de LausanneCentre Hospitalier Universitaire Vaudois
KeywordsCandida albicansFungal pathogenSulfurMicrobiologyPathogenBiologyAntifungalChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Although considerable advances have been made in understanding the metabolic machinery that enables bacteria to utilize sulfur sources, our understanding of the corresponding processes in fungi remains comparatively fragmentary. To explore the genetic circuit by which the highly prevalent human opportunistic yeast Candida albicans controls sulfur utilization, we characterized the transcriptional landscape associated with sulfur starvation in this fungus. We identified many desulfonation enzymes that were differentially modulated and showed that Jlp12, a sulfonate/α-ketoglutarate dioxygenase, was critical for the utilization of different sulfur sources found in many niches of the human host. We also uncovered that the zinc-finger transcription factor Met32 acts as a master regulator, modulating genes involved in sulfur utilization, including Jlp12. Importantly, we found that C. albicans Met32 exclusively regulates sulfur utilization genes, while in the Saccharomyces cerevisiae lineage, it controls methionine biosynthesis. This work also identified Seo13 as the first major facilitator superfamily transporter in fungi to transport the alternative sulfur source glutathione, under the direct control of Met32. Furthermore, we showed that Met32 modulates C. albicans tolerance to sulfite excess by tuning the basal transcriptional level of the superoxide dismutase Sod1. This underscores the dual role of Met32 in the breakdown of sulfur-containing metabolites and the neutralization of the resulting reactive sulfur species (RSS). Our study delineates a new mechanism by which fungal pathogens utilize sulfur sources and neutralize RSS and underscores its importance in fungal fitness in vivo . IMPORTANCE Candida albicans is the most prevalent fungal colonizer of humans, and it is also the first cause of disseminated fungal infections leading to a high mortality rate. The ability of this yeast to metabolize a plethora of carbon and nitrogen sources inside the host is a critical asset for both the commensal and the pathogenic lifestyles of this yeast. Thus, these pathways represent attractive targets for antifungal therapy. While sulfur is an essential nutritional element for all living organisms, its contribution to fungal virulence remains understudied. Here, we describe new players of sulfur utilization metabolism in C. albicans and underline their importance in supporting fungal virulence. This work emphasizes the significance of targeting sulfur metabolic flexibility to manage fungal infections.

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 score0.001
Threshold uncertainty score0.003

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 head0.020
GPT teacher head0.257
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

Citations1
Published2025
Admission routes2
Has abstractyes

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