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Record W4391963812 · doi:10.1128/spectrum.04095-23

A chemical screen identifies structurally diverse metal chelators with activity against the fungal pathogen <i>Candida albicans</i>

2024· article· en· W4391963812 on OpenAlexafffund
Sara Fallah, Dustin Duncan, Kyle D. Reichl, Michael J. Smith, Wenyu Wang, John A. Porco, Lauren E. Brown, Luke Whitesell, Nicole Robbins, Leah E. Cowen

Bibliographic record

VenueMicrobiology Spectrum · 2024
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsBrock UniversityUniversity of Toronto
FundersNational Center for Advancing Translational SciencesCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute of General Medical SciencesCanadian Institute for Advanced Research
KeywordsCandida albicansCorpus albicansMicrobiologyAspergillus fumigatusXanthoneBiologyCandida glabrataPathogenAntifungal drugEffluxEchinocandinsNatural productAmphotericin BChemistryBiochemistryAntifungalCaspofungin

Abstract

fetched live from OpenAlex

ABSTRACT Candida albicans , one of the most prevalent human fungal pathogens, causes diverse diseases extending from superficial infections to deadly systemic mycoses. Currently, only three major classes of antifungal drugs are available to treat systemic infections: azoles, polyenes, and echinocandins. Alarmingly, the efficacy of these antifungals against C. albicans is hindered both by basal tolerance toward the drugs and the development of resistance mechanisms such as alterations of the drug’s target, modulation of stress responses, and overexpression of efflux pumps. Thus, the need to identify novel antifungal strategies is dire. To address this challenge, we screened 3,049 structurally-diverse compounds from the Boston University Center for Molecular Discovery (BU-CMD) chemical library against a C. albicans clinical isolate and identified 17 molecules that inhibited C. albicans growth by &gt;80% relative to controls. Among the most potent compounds were CMLD013360, CMLD012661, and CMLD012693, molecules representing two distinct chemical scaffolds, including 3-hydroxyquinolinones and a xanthone natural product. Based on structural insights, CMLD013360, CMLD012661, and CMLD012693 were hypothesized to exert antifungal activity through metal chelation. Follow-up investigations revealed all three compounds exerted antifungal activity against non-albicans Candida , including Candida auris and Candida glabrata , with the xanthone natural product CMLD013360 also displaying activity against the pathogenic mould Aspergillus fumigatus . Media supplementation with metallonutrients, namely ferric or ferrous iron, rescued C. albicans growth, confirming these compounds act as metal chelators. Thus, this work identifies and characterizes two chemical scaffolds that chelate iron to inhibit the growth of the clinically relevant fungal pathogen C. albicans IMPORTANCE The worldwide incidence of invasive fungal infections is increasing at an alarming rate. Systemic candidiasis caused by the opportunistic pathogen Candida albicans is the most common cause of life-threatening fungal infection. However, due to the limited number of antifungal drug classes available and the rise of antifungal resistance, an urgent need exists for the identification of novel treatments. By screening a compound collection from the Boston University Center for Molecular Discovery (BU-CMD), we identified three compounds representing two distinct chemical scaffolds that displayed activity against C. albicans . Follow-up analyses confirmed these molecules were also active against other pathogenic fungal species including Candida auris and Aspergillus fumigatus . Finally, we determined that these compounds inhibit the growth of C. albicans in culture through iron chelation. Overall, this observation describes two novel chemical scaffolds with antifungal activity against diverse fungal pathogens.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.664

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.234
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 teacher head, 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

Citations10
Published2024
Admission routes2
Has abstractyes

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