Deletion of Aspergillus nidulans GDP-mannose transporters affects hyphal morphometry, cell wall architecture, spore surface character, cell adhesion, and biofilm formation
Bibliographic record
Abstract
Systemic human fungal infections are increasingly common. Aspergillus species cause most of the airborne fungal infections. Life-threatening invasive aspergillosis was formerly found only in immune-suppressed patients, but recently some strains of A. fumigatus have become primary pathogens. Many fungal cell wall components are absent from mammalian systems, so they are potential drug targets. Cell-wall-targeting drugs such as echinocandins are used clinically, although echinocandin-resistant strains were discovered shortly after their introduction. Currently there are no fully effective anti-fungal drugs. Fungal cell wall glycoconjugates modulate human immune responses, as well as fungal cell adhesion, biofilm formation, and drug resistance. Guanosine diphosphate (GDP) mannose transporters (GMTs) transfer GDP-mannose from the cytosol to the Golgi lumen prior to mannosylation. Aspergillus nidulans GMTs are encoded by gmtA and gmtB. Here we elucidate the roles of A. nidulans GMTs. Strains engineered to lack either or both GMTs were assessed for hyphal and colonial morphology, cell wall ultrastructure, antifungal susceptibility, spore hydrophobicity, adherence and biofilm formation. The gmt-deleted strains had smaller colonies with reduced sporulation and with thicker hyphal walls. The gmtA deficient spores had reduced hydrophobicity and were less adherent and less able to form biofilms in vitro. Thus, gmtA not only participates in maintaining the cell wall integrity but also plays an important role in biofilm establishment and adherence of A. nidulans. These findings suggested that GMTs have roles in A. nidulans growth and cell-cell interaction and could be a potential target for new antifungals that target virulence determinants.
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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".