Aspergillus fumigatus AcuM regulates both iron acquisition and gluconeogenesis
Bibliographic record
Abstract
Relatively few transcription factors that govern the virulence of Aspergillus fumigatus are known. We constructed 11 A. fumigatus transcription factor mutants and screened them for altered virulence in Galleria mellonella larvae. We discovered that the zinc cluster transcription factor, AcuM, is essential for maximal virulence in this model, as well as in murine models of haematogenously disseminated and invasive pulmonary aspergillosis. Transcriptional profiling experiments suggested that AcuM suppresses sreA and induces hapX to stimulate expression of genes involved in both reductive iron assimilation and siderophore-mediated iron uptake. Consistent with these results, a ΔacuM mutant had reduced iron incorporation, decreased extracellular siderophore production and impaired capacity to grow under iron-limited conditions. Interestingly, an Aspergillus nidulansΔacuM mutant had normal extracellular siderophore production and growth under iron-limited conditions, indicating that AcuM does not govern iron acquisition in this organism. A. fumigatus AcuM also regulated genes involved in gluconeogenesis, and the ΔacuM mutant had impaired growth on gluconeogenic carbon sources. Deletion of sreA in the ΔacuM mutant restored iron uptake, extracellular siderophore production and virulence, but not the defect in gluconeogenesis. Thus, AcuM represses SreA and thereby induces iron acquisition, a process that is essential for the maximal virulence of A. fumigatus.
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 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) | 0.001 | 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) | 0.000 | 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".