Targeting bacterial nickel transport with aspergillomarasmine A suppresses virulence-associated Ni-dependent enzymes
Bibliographic record
Abstract
Abstract Microbial Ni 2+ homeostasis underpins the virulence of several clinical pathogens. Ni 2+ is an essential cofactor in urease and [NiFe]-hydrogenases involved in colonization and persistence. Many microbes produce metallophores to sequester metals necessary for their metabolism and starve competing neighboring organisms. The fungal metallophore aspergillomarasmine A (AMA) shows narrow specificity for Zn 2+ , Ni 2+ , and Co 2+ . Here, we show that this specificity allows AMA to block the uptake of Ni 2+ and attenuate bacterial Ni-dependent enzymes, offering a potential strategy for reducing virulence. Bacterial exposure to AMA perturbs H 2 metabolism, ureolysis, struvite crystallization, and biofilm formation and shows efficacy in a Galleria mellonella animal infection model. The inhibition of Ni-dependent enzymes was aided by Zn 2+, which complexes with AMA and competes with the native nickelophore for the uptake of Ni 2+ . Biochemical analyses demonstrated high-affinity binding of AMA-metal complexes to NikA, the periplasmic substrate-binding protein of the Ni 2+ uptake system. Structural examination of NikA in complex with Ni-AMA revealed that the coordination geometry of Ni-AMA mimics the native ligand, Ni-( l -His) 2 , providing a structural basis for binding AMA-metal complexes. Structure-activity relationship studies of AMA identified regions of the molecule that improve NikA affinity and offer potential routes for further developing this compound as an anti-virulence agent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".