A broad-spectrum anti-fungal effector dictates bacterial-fungal interkingdom interactions
Bibliographic record
Abstract
Bacteria-fungi interactions play crucial roles in shaping microbial communities across diverse environmental and host-associated niches. While their antagonism through diffusible metabolites is a well-known ecological phenomenon, delivery of bacterial effectors into the nucleus of fungal cells remains rare, and the mechanisms are poorly understood. Here, we identify and characterize a potent anti-fungal nuclease effector, TseN, delivered by the type VI secretion system (T6SS) of Acidovorax citrulli. TseN possesses a nuclear localization signal and inhibits multiple fungal species, including emerging human pathogens Candida auris and Cryptococcus neoformans. Structural and biochemical analyses show that TseN possesses a unique C-terminal DNase domain that defines a new effector class, with its activity neutralized by a cognate immunity protein TsiN. The delivery of TseN requires the upstream-encoded VgrG5, a chaperone Aave_2128, and PAAR5. Transcriptome profiling of co-cultured bacterial-fungal cells demonstrates that the T6SS attack triggers extensive reprogramming in fungal cells, affecting DNA repair, stress response, and filamentation pathways. These responses not only compromise fungal survival but also modulate fungal drug resistance, as evidenced by the synergistic enhancement of azole efficacy against drug-resistant Candida albicans. Importantly, in vivo experiments confirm that the T6SS, via TseN, can significantly reduce fungal burden on murine skin. Phylogenetic analysis shows TseN homologs are present in a large number of bacterial species. Collectively, our findings highlight a previously underappreciated interkingdom antagonism modulated by a novel effector. The broad-spectrum anti-fungal activities of TseN and its homologs may be explored for therapeutic strategies targeting fungal pathogens in both clinical and environmental settings.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".