Genome‐Wide Screen for <i>Escherichia coli</i> [NiFe]‐Hydrogenase Maturation Factors
Bibliographic record
Abstract
[NiFe]‐hydrogenases catalyze the reversible oxidation of hydrogen gas at an intricate bimetallic active site to facilitate the anaerobic growth of many bacteria and archaea.(1) The maturation of this enzyme relies on a network of cellular pathways: for instance, the incorporation of nickel and iron cofactors both rely on specific metallochaperones as well as their respective metal homeostasis networks.(2) Therefore, [NiFe]‐hydrogenase activity can serve as a beacon for many related biochemical pathways. Furthermore, the ability to tune [NiFe]‐hydrogenase activity has garnered much interest for potential applications to the hydrogen economy and as a novel antibiotic target.(3,4) However, current methodologies to measure hydrogenase activity are time consuming and require specialized equipment, limiting discoveries of novel hydrogenase‐related applications. In order to answer these issues, we designed, optimized, and validated a [NiFe]‐hydrogenase assay in E. coli that is amenable to high‐throughput screening. We have applied this assay to screen the Keio collection(5) in search of the remaining genes that contribute, directly or indirectly, to [NiFe]‐hydrogenase maturation. Here we report the discovery and characterization of genes that had no previously known function. Support or Funding Information This work was supported in part by funding from the Natural Science and Engineering Research Council (Canada) and the Canadian Institutes of Health Research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".