A whole-cell, high-throughput hydrogenase assay to identify factors that modulate [NiFe]-hydrogenase activity
Bibliographic record
Abstract
[NiFe]-hydrogenases have attracted attention as potential therapeutic targets or components of a hydrogen-based economy. [NiFe]-hydrogenase production is a complicated process that requires many associated accessory proteins that supply the requisite cofactors and substrates. Current methods for measuring hydrogenase activity have low throughput and often require specialized conditions and reagents. In this work, we developed a whole-cell high-throughput hydrogenase assay based on the colorimetric reduction of benzyl viologen to explore the biological networks of these enzymes in Escherichia coli . We utilized this assay to screen the Keio collection, a set of nonlethal single-gene knockouts in E. coli BW25113. The results of this screen highlighted the assay's specificity and revealed known components of the intricate network of systems that underwrite [NiFe]-hydrogenase activity, including nickel homeostasis and formate dehydrogenase activities as well as molybdopterin and selenocysteine biosynthetic pathways. The screen also helped identify several new genetic components that modulate hydrogenase activity. We examined one E. coli strain with undetectable hydrogenase activity in more detail (Δ eutK ), finding that nickel delivery to the enzyme active site was completely abrogated, and tracked this effect to an ancillary and unannotated lack of the fumarate and nitrate reduction (FNR) anaerobic regulatory protein. Collectively, these results demonstrate that the whole-cell assay developed here can be used to uncover new information about bacterial [NiFe]-hydrogenase production and to probe the cellular components of microbial nickel homeostasis.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".