Disarming Fungal Pathogens: <i>Bacillus safensis</i> Inhibits Virulence Factor Production and Biofilm Formation by <i>Cryptococcus neoformans</i> and <i>Candida albicans</i>
Bibliographic record
Abstract
ABSTRACT Bacteria interact with each other in nature and often compete for limited nutrient and space resources. However, it is largely unknown whether and how bacteria also interact with human fungal pathogens naturally found in the environment. Here, we identified a soil bacterium, Bacillus safensis , which potently blocked several key Cryptococcus neoformans virulence factors, including formation of the antioxidant pigment melanin and production of the antiphagocytic polysaccharide capsule. The bacterium also inhibited de novo cryptococcal biofilm formation but had only modest inhibitory effects on already formed biofilms or planktonic cell growth. The inhibition of fungal melanization was dependent on direct cell contact and live bacteria. B. safensis also had anti-virulence factor activity against another major human-associated fungal pathogen, Candida albicans . Specifically, dual-species interaction studies revealed that the bacterium strongly inhibited C. albicans filamentation and biofilm formation. In particular, B. safensis physically attached to and degraded candidal filaments. Through genetic and phenotypic analyses, we demonstrated that bacterial chitinase activity against fungal cell wall chitin is a factor contributing to the antipathogen effect of B. safensis . IMPORTANCE Pathogenic fungi are estimated to contribute to as many human deaths as tuberculosis or malaria. Two of the most common fungal pathogens, Cryptococcus neoformans and Candida albicans , account for up to 1.4 million infections per year with very high mortality rates. Few antifungal drugs are available for treatment, and development of novel therapies is complicated by the need for pathogen-specific targets. Therefore, there is an urgent need to identify novel drug targets and new drugs. Pathogens use virulence factors during infection, and it has recently been proposed that targeting these factors instead of the pathogen itself may represent a new approach to develop antimicrobials. Here, we identified a soil bacterium that specifically blocked virulence factor production and biofilm formation by C. neoformans and C. albicans . We demonstrate that the bacterial antipathogen mechanism is based in part on targeting the fungal cell wall, a structure not found in human cells.
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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".