Genetic Engineering of <i>Acidithiobacillus ferridurans</i> Using CRISPR Systems To Mitigate Toxic Release in Biomining
Bibliographic record
Abstract
Biomining processes utilize microorganisms, such as Acidithiobacillus, to extract valuable metals by producing sulfuric acid and ferric ions that dissolve sulfidic minerals. However, excessive production of these compounds can result in metal structure corrosion and groundwater contamination. Synthetic biology offers a promising solution to improve Acidithiobacillus strains for sustainable, eco-friendly, and cost-effective biomining, but genetic engineering of these slow-growing microorganisms is challenging with current inefficient and time-consuming methods. To address this, we established a CRISPR-dCas9 system for gene knockdown in A. ferridurans JAGS, successfully downregulating the transcriptional levels of two genes involved in sulfur oxidation. More importantly, we constructed an all-in-one CRISPR-Cas9 system for fast and efficient genome editing in A. ferridurans JAGS, achieving seamless gene deletion ( HdrB3 ), promoter substitution (Prus to Ptac), and exogenous gene insertion ( GFP ). Additionally, we created a HdrB-Rus double-edited strain and performed biomining experiments to extract Ni from pyrrhotite tailings. The engineered strain demonstrated a similar Ni recovery rate to wild-type A. ferridurans JAGS but with significantly lower production of iron ions and sulfuric acid in leachate. These high-efficient CRISPR systems provide a powerful tool for studying gene functions and creating useful recombinants for synthetic biology-assisted biomining applications in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".