The mitochondrial thiamine pyrophosphate transporter TptA promotes adaptation to low iron conditions and virulence in fungal pathogen <i>Aspergillus fumigatus</i>
Bibliographic record
Abstract
Aspergillus fumigatus is the most prevalent airborne fungal pathogen that causes invasive fungal infections in immunosuppressed individuals. Adaptation to iron limited conditions is crucial for A. fumigatus virulence. To identify novel genes that play roles in adaptation to low iron conditions we performed an insertional mutagenesis screen in A. fumigatus. Using this approach, we identified the tptA gene in A. fumigatus, which shares homology with the Saccharomyces cerevisiae thiamine pyrophosphate (ThPP) transporter encoding gene tpc1. Heterologous expression of tpc1 in the tptA deletion mutant completely restored the ThPP auxotrophy phenotype, suggesting that Tpc1 and TptA are functional orthologues. Importantly, TptA was required for adaptation to low iron conditions in A. fumigatus. The ΔtptA mutant had decreased resistance to the iron chelator bathophenanthroline disulfonate (BPS) with severe growth defects. Moreover, loss of tptA decreased the expression of hapX, which is a major transcription factor indispensable for adaptation to iron starvation in A. fumigatus. Overexpression of hapX in the ΔtptA strain greatly rescued the growth defect and siderophore production by A. fumigatus in iron-depleted conditions. Mutagenesis experiments demonstrated that the conserved residues related to ThPP uptake in TptA were also required for low iron adaptation. Furthermore, TptA-mediated adaptation to low iron conditions was found to be dependent on carbon sources. Finally, loss of tptA resulted in the attenuation of virulence in a murine model of aspergillosis. Taken together, this study demonstrated that the mitochondrial ThPP transporter TptA promotes low iron adaptation and virulence in A. fumigatus.
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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".