An examination of the quinic acid utilization genes in <i>Aspergillus niger</i> reveals the involvement of 2 pH-dependent permeases
Bibliographic record
Abstract
Many microorganisms are able to use plant-derived aromatic and cyclic compounds like the common plant secondary metabolite quinic acid as carbon and energy sources. In fungi, 3 enzymatic steps convert quinic acid into the common intermediate protocatechuic acid, which is then further converted into tricarboxylic acid cycle intermediates. The genes encoding these 3 enzymes are known to be part of a gene cluster in Neurospora crassa along with a permease, a gene of unknown function, and an activator-repressor module controlling expression of the cluster. This gene cluster is conserved in fungi and has also been studied in Aspergillus nidulans, where an additional gene of unknown function is included. Here, we studied these genes in the filamentous fungus Aspergillus niger, where the availability of high-quality, well-annotated genomes and efficient tools for genome-editing and global gene expression analysis could provide new insights into quinic acid utilization in fungi. Using homology and whole transcriptome sequencing, we identified the genes involved in quinic acid utilization. Knockout mutants of these genes were then created to observe the growth phenotype on quinic acid media. We showed that not all the genes involved in quinic acid utilization in A. niger are linked. In addition to the in-cluster permease gene, we identified a second, previously unknown off-cluster permease gene which was upregulated in the presence of quinic acid. These 2 permeases were determined to function optimally at different pH levels, with the in-cluster permease being more effective at pH 6.5 and the off-cluster permease more effective at pH 3.5.
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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".