Role of Aspergillus niger <i>acrA</i> in Arsenic Resistance and Its Use as the Basis for an Arsenic Biosensor
Bibliographic record
Abstract
Arsenic contamination of groundwater sources is a major issue worldwide, since exposure to high levels of arsenic has been linked to a variety of health problems. Effective methods of detection are thus greatly needed as preventive measures. In an effort to develop a fungal biosensor for arsenic, we first identified seven putative arsenic metabolism and transport genes in Aspergillus niger, a widely used industrial organism that is generally regarded as safe (GRAS). Among the genes tested for RNA expression in response to arsenate, acrA, encoding a putative plasma membrane arsenite efflux pump, displayed an over 200-fold increase in gene expression in response to arsenate. We characterized the function of this A. niger protein in arsenic efflux by gene knockout and confirmed that AcrA was located at the cell membrane using an enhanced green fluorescent protein (eGFP) fusion construct. Based on our observations, we developed a putative biosensor strain containing a construct of the native promoter of acrA fused with egfp. We analyzed the fluorescence of this biosensor strain in the presence of arsenic using confocal microscopy and spectrofluorimetry. The biosensor strain reliably detected both arsenite and arsenate in the range of 1.8 to 180 μg/liter, which encompasses the threshold concentrations for drinking water set by the World Health Organization (10 and 50 μg/liter).
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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) | 0.000 | 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".